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Rosemont Copper Company

AERMOD Modeling Protocol to Assess

Ambient Air Quality Impacts

Prepared for:

Rosemont Copper Company

3031 W. Ina Road

Tucson, AZ 80246

Contact: Kathy Arnold

Prepared by:

JBR Environmental Consultants, Inc.

1553 W. Elna Rae, Ste. 101

Tempe, AZ 85281

Contact: Louis Thanukos

480.829.0457

April 2012

Rosemont AERMOD Modeling Protocol ii

TABLE OF CONTENTS

Page

1. INTRODUCTION ............................................................................................................................... 1

1.1 Project Overview ....................................................................................................................... 1

1.2 Purpose of AERMOD Modeling and Submittal of Modeling Protocol........................................ 1

1.3 Operational Changes Planned Since Prior Submittals .............................................................. 2

1.4 Facility Description .................................................................................................................... 3

1.5 Site Description ......................................................................................................................... 3

2. AIR QUALITY REGULATORY FRAMEWORK ................................................................................. 5

2.1 Rosemont Area Air Quality Classifications ................................................................................ 5

2.2 Source Designation ................................................................................................................... 5

2.3 Air Quality Regulatory Authority ................................................................................................ 6

3. DISPERSION MODELING INPUT DATA AND DEFAULTS ............................................................. 7

3.1 Recommended Regulatory Default Options .............................................................................. 8

3.2 Missing Data Processing Routines ............................................................................................ 8

3.3 Regional Topography ................................................................................................................ 8

3.4 Rural/Urban Classification ......................................................................................................... 8

3.5 Regional Climatology ................................................................................................................. 9

3.6 Meteorological Monitoring for On-Site Data .............................................................................. 9

3.7 Meteorological Data Processing for AERMOD ........................................................................ 10

3.8 Sky Cover Data ....................................................................................................................... 10

3.9 Upper Air and Surface Meteorological Data ............................................................................ 10

3.10 Surface Characteristics .......................................................................................................... 10

4. BACKGROUND CONCENTRATIONS ............................................................................................ 16

4.1 PM10 ......................................................................................................................................... 16

4.2 PM2.5 ........................................................................................................................................ 19

4.3 NO2 .......................................................................................................................................... 20

4.4 CO...........................................................................................................................................21

4.5 SO2 .......................................................................................................................................... 22

4.6 Pollutant Transport to Rosemont ............................................................................................. 22

5. MODELING ANALYSIS DESIGN .................................................................................................... 25

5.1 Ozone Limiting Method for Evaluating NO2 Impacts ............................................................... 25

5.2 Receptor Network .................................................................................................................... 26

5.3 Receptor Elevations ................................................................................................................ 27

Rosemont AERMOD Modeling Protocol iii

5.4 Modeling Domain ..................................................................................................................... 27

5.5 Plume Depletion ...................................................................................................................... 27

5.6 Building Downwash ................................................................................................................. 27

6. EMISSIONS MODELED AND SOURCE CHARACTERIZATION ................................................... 30

6.1 Operational Years to Be Modeled ........................................................................................... 30

6.1.1 Annual Criteria Pollutant Emissions Modeling............................................................ 30

6.1.2 Short-Term Criteria Pollutant Emissions Modeling .................................................... 30

6.2 Point Sources .......................................................................................................................... 33

6.3 Volume Sources ...................................................................................................................... 33

6.3.1 Roads ......................................................................................................................... 33

6.3.2 Truck Unloading ......................................................................................................... 34

6.3.3 Sulfide Ore Stockpile .................................................................................................. 34

6.3.4 Tailings Stockpile ........................................................................................................ 34

6.3.5 Conveyor Transfer Points ........................................................................................... 34

6.3.6 Gaseous Emissions Due to Blasting .......................................................................... 35

6.3.7 Open Pit ...................................................................................................................... 35

6.3.8 Tail Pipe Emissions .................................................................................................... 36

7. ALTERNATIVE SCENARIOS .......................................................................................................... 37

8. EVALUATION OF DISPERSION MODELING RESULTS .............................................................. 38

8.1 CO Evaluation.......................................................................................................................... 38

8.2 NO2 Evaluation ........................................................................................................................ 38

8.3 PM10 Evaluation ....................................................................................................................... 38

8.4 SO2 Evaluation ........................................................................................................................ 38

8.5 PM2.5 Evaluation ...................................................................................................................... 39

Rosemont AERMOD Modeling Protocol iv

APPENDICES

APPENDIX A: PARTICLE SIZE DISTRIBUTIONS

APPENDIX B: QUARTERLY PM10 MONITORING SUMMARIES

APPENDIX C: STATISTICAL EVALUATION OF AMBIENT PM10 MEASUREMENTS

APPENDIX C.1: MINITAB OUTPUT

APPENDIX D: AVERAGE AND MAXIMUM MINING RATES

APPENDIX E: CORRESPONDENCE WITH ADEQ

APPENDIX F: IN-STACK NO2/NOX RATIO JUSTIFICATION

APPENDIX G: CHANGES IN EMISSIONS FROM PRIOR SUBMITTALS

Rosemont AERMOD Modeling Protocol v

TABLES

Table 3.1 Surface Characteristics Proposed for Use in the AERMOD Modeling ............................. 11

Table 4.1 PM10 Monitoring Results ................................................................................................... 17

Table 4.2 PM10 Annual Average Monitored Concentrations ............................................................. 19

Table 4.3 Maximum Monitored Value of the One-Hour Average NO2 Concentration (ppm) ........... 21

Rosemont AERMOD Modeling Protocol vi

FIGURES

Figure 1.1 General location map of Rosemont and surrounding area. ................................................ 4

Figure 3.1 Topographic Map Showing Location of the PM10 and Meteorological Monitoring Sites. .. 12

Figure 3.2 Wind Rose for the Rosemont Meteorological Station for the Time Period

April 1, 2006 – March 31, 2007. ........................................................................................ 13


April 1, 2007 - March 31, 2008. ......................................................................................... 14


April 1, 2008 - March 31, 2009. ......................................................................................... 15

Figure 4.1 Wind Rose for the Tucson Airport ..................................................................................... 23

Figure 5.1 Receptor Grid Network Developed for the Rosemont Project Modeling Analysis ............ 28

Figure 5.2 Receptor Network for Evaluating Impacts at the Saguaro East National Park ................. 29

Figure 6.1 Plan View Map of Operations Depicting Facility Layout by Year 5 ................................... 31

Figure 6.2 Plan View Map of Operations Showing Updated Ancillary Operation Locations for

Rosemont .......................................................................................................................... 32

Rosemont AERMOD Modeling Protocol 1

1. INTRODUCTION

This document, “AERMOD Modeling Protocol to Assess Ambient Air Quality Impacts,” is being

submitted to the U.S. Department of Agriculture Forest Service (USFS or Forest Service), Coronado

National Forest (the Coronado), on behalf of Rosemont Copper Company. Pursuant to the National

Environmental Quality Act (NEPA), the Forest Service is the lead agency for preparing the

Environmental Impact Statement (EIS) for the proposed Rosemont project, and land manager for the

Forest Service at the Coronado National Forest that prepared the draft EIS (DEIS) issued in

September 2011. Based on comments received in response to the DEIS, changes have been made

to the air quality modeling protocol that will be used to model and assess the ambient air quality

impacts of the Rosemont project for presentation in the final EIS.

1.1 Project Overview

The proposed Rosemont Copper Company Project (Rosemont) consists of an open-pit copper mine

and its associated copper production activities. The proposed mine site is located on the east side of

the Santa Rita Mountains, approximately 30 miles south of Tucson, Arizona in Pima County (). The

preliminary mine plan of operations (MPO) was submitted to the Coronado in July 2007 (available at

www.rosemontcopper.com). As proposed, the operation would directly impact National Forest System

land on the Coronado National Forest in addition to private land owned by the Rosemont Copper

Company, State Trust land administered by the Arizona State Land Department, and Bureau of Land

Management-administered lands. While the Forest Service is the lead agency for the EIS, there are

nearly twenty cooperating agencies that have been provided the opportunity to comment on the air

modeling protocol and DEIS.

1.2 Purpose of AERMOD Modeling and Submittal of Modeling Protocol

Construction, mining, and reclamation activities at the mine would increase emissions in the affected

area. Therefore, air quality modeling is being performed to identify, to the extent feasible, what

impact those emissions would have on ambient air quality. The Federal Land Manager (FLM)

requested that an air impact analysis be submitted as part of the EIS in order to demonstrate that the

National Ambient Air Quality Standards (NAAQS) will be protected.

In October 2009, Rosemont submitted the “Modeling Protocol to Asses Ambient Air Quality Impacts

from the Rosemont Copper Project.” A modeling analysis titled “Modeling Report to Asses Ambient

Air Quality Impacts” was subsequently submitted on July 28, 2010. Comments to this modeling

analysis were provided by the Forest Service on February 25, 2011. Additional comments as well as

recommendations for addressing potential concerns were discussed during conference calls on

March 14, 25, 29 and 31, 2011. A revised modeling report was submitted to the Forest Service on

April 4, 2011, and the Forest Service issued the DEIS in September 2011. In order to respond to

questions and comments received by the cooperating agencies regarding the ambient air quality

modeling, and to incorporate additional mitigation measures that Rosemont will implement that

reduce emissions of pollutants, further ambient air quality modeling will be performed prior to the

Forest Service issuing a final EIS. This document presents the protocol that will be followed for the

revised modeling as requested by the Forest Service and cooperating agencies.

http://www.rosemontcopper.com/


The modeling protocol presented herein incorporates changes in emissions due to the additional

mitigation measures and is also intended to addresses the questions, comments, and

recommendations made by the Forest Service and cooperating agencies. The remaining sections of

this report present the protocol that will be followed to assess ambient air quality impacts from the

proposed project. This protocol has been developed following recommendations of the Forest

Service and cooperating agencies and taking into consideration the precedents set forth in the

Arizona Department of Environmental Quality (ADEQ) guidance document Air Dispersion Modeling

Guidelines for Air Quality Permits (ADEQ Guidance, December 2004) and the EPA’s Guideline on Air

Quality Models (Guidelines, 40 CFR Part 51, Appendix W, November 2005). Additional references

taken into consideration include EPA’s Meteorological Monitoring Guidance for Regulatory Modeling

Applications (February 2000) and guidance documents available through EPA’s Technology Transfer

Network (TTN) Support Center for Regulatory Atmospheric Modeling (SCRAM) website at

http://www.epa.gov/ttn/scram/.

1.3 Operational Changes Planned Since Prior Submittals

Since submittal of the previous modeling analyses, Rosemont has re-evaluated its proposed

operations and will be making the following changes that affect particulate matter (PM) and gaseous

emissions and the resulting predicted impacts:

Six of the haul trucks will have Tier 4 engines rather than Tier 2 engines

The entry road will be paved (a distance of 3.1 miles) as will access and main roads that are

not traveled by haul trucks

Changes to the lime systems, including slaking all lime in two lime slakers (controlled by a

scrubber) prior to distribution to various processes

Seven cartridge filter dust collectors will be installed in lieu of the six less-efficient wet

scrubbers, and a cartridge filter dust collector will be installed for the molybdenum dust

collector

The resulting change in the potential to emit (PTE) for PM less than 10 microns in diameter (PM10) for

fugitive, non-fugitive, and tailpipe emissions combined is a reduction of 52 tons per year (tpy) in

Year 5. For PM less than 2.5 microns in diameter (PM2.5), the combined reduction in fugitive, non-

fugitive, and tailpipe emissions is 47 tpy. These numbers represent a 5% reduction of PM10 and a

25% reduction of PM2.5 emissions for fugitive, non-fugitive, and tailpipe emissions combined (based

on Year 5). Non-fugitive emissions of PM10 and PM2.5 will be reduced by 42% and 81%, respectively

in Year 5. Details of the changes in emissions for each category for different project years are

provided in Appendix G. Facility-wide emissions of oxides of nitrogen (NOx) will be reduced by 70 tpy

and volatile organic compound (VOC) emissions will be reduced by 6 tpy in Year 5 with the planned

operational changes.

http://www.epa.gov/ttn/scram/


1.4 Facility Description

The Rosemont project includes an open-pit mine and ore processing operations comprised of milling,

copper concentrating, copper leaching, and solvent extraction/electrowinning. No copper smelting is

included in the project, nor is any connection to any existing copper smelter under consideration. The

production schedule developed from mining sequence plans indicates a project operating life of

approximately 20-25 years using only proven and probable mineral reserves.

Peak mining rates of approximately 115,000,000 tons per year (tpy) of total material (ore and waste)

could be anticipated in Year 1. During this year of operation, however, operations would still be in the

development stages. Once full-scale operation has been achieved, maximum mining rates during

Years 2-10 are estimated by including a 20% capacity factor above the average mining capacity. For

Years 2-10 the maximum mining rate is expected to be approximately 110,000,000 tpy of total

material. Mining rates are expected to taper off during the remaining years of the project.

Mining of the ore will be through conventional open-pit mining techniques including drilling, blasting,

loading, hauling and unloading. Waste rock will be transported by haul truck to the waste rock

storage areas. Ore will be either transported by haul truck to the leach pad (oxide ore), or crushed

and loaded onto a conveyor for transport to the mill (sulfide ore). The copper and molybdenum

concentrates from the milling and flotation operations will be shipped off-site for further processing.

Oxide ore will be placed on the lined leach pad. Pregnant leach solution (PLS) from the pad will be

collected in a solution pond and then processed through the SX/EW plant. Copper cathodes

generated from the SX/EW plant will be transported off-site for further processing.

1.5 Site Description

Rosemont will be located in Pima County, approximately 30 miles southeast of Tucson, Arizona

(Arizona Geological Society 2007:11) as shown in . Regionally, the facility location is in the eastern

part of the Sonoran Desert sub-province of the Basin and Range physiographic province (Arizona

Geological Society 2007:26), near the boundary with the Mexican Highlands. The area is

characterized by northerly trending fault block mountains separated by broad, down-faulted valleys

(see Figures 1.1, 3.1 and 5.1) on the eastern slope of the Santa Rita Mountains, a range that

separates the Cienega Basin to the east from the Santa Cruz Basin to the west. The site is at an

elevation of approximately 5,350 feet with elevations in the project area range from 4,600 feet to

nearly 6,300 feet above mean sea level. Slope angles vary from less than 3 percent in drainage

bottoms to more than 100 percent on the rock faces of some mountain fronts.

Areas where mine activities take place, including the open pit, waste rock storage area, tailings area,

heap leach facility, plant site and ancillary facilities, and mine primary and secondary access roads

will be excluded from public access by fencing and signage. These areas will not be accessible to

the public and the boundaries will be formally and legally established through the EIS process.


Figure 1.1 General location map of Rosemont and surrounding area.


2. AIR QUALITY REGULATORY FRAMEWORK

2.1 Rosemont Area Air Quality Classifications

EPA classifies air quality regions as “nonattainment” for a given pollutant if ambient air concentrations

exceed the National Ambient Air Quality Standards (NAAQS). NAAQS are established separately for

each of the “criteria” pollutants and these NAAQS have been promulgated under Title 40 of the Code

of Federal Regulations (40 CFR) Part 50 (see http://www.epa.gov/air/criteria.html for more

information). Areas that are not nonattainment are either “attainment” if the NAAQS have not been

exceeded, or the area is deemed unclassifiable/attainment if insufficient data exists to make a

determination. Attainment status is based on the results of ambient air quality monitoring, typically

performed over a 3-year period.

The Rosemont area is classified as attainment or unclassifiable/attainment for particulate matter,

represented as both PM10 and PM less than 2.5 microns nominal aerodynamic diameter (PM2.5), as

well as lead (Pb), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)

(see 40 CFR §81.303 for the promulgated attainment status of all areas in Arizona, or

http://www.epa.gov/oaqps001/greenbk/ for maps identifying nonattainment areas throughout Arizona

and the United States). Each of the criteria pollutants may be directly emitted from a source, with the

exception of ozone, which is produced by a complex photochemical reaction of volatile organic

compounds (VOCs) and NOX in the lower atmosphere.

2.2 Source Designation

New stationary sources located in attainment areas are subject to air quality permitting under

Prevention of Significant Deterioration (PSD) as promulgated under 40 CFR Part 52 if the potential to

emit of PM10, PM2.5, NOx, SO2, or CO exceed 250 tpy. Rosemont is not a PSD source. (While

emissions of other pollutants also trigger PSD, the pollutants listed are of interest for the purposes of

this modeling protocol.) PSD permitting involves a number of requirements, one of which is an air

quality impact analysis involving dispersion modeling. Rosemont’s emissions are well below the PSD

thresholds for all pollutants, so PSD does not apply. However, since the PSD program does provide

a long-standing, nationally-standardized framework for performing ambient air quality monitoring and

dispersion modeling, the PSD methodologies will generally be applied for the modeling at Rosemont

and have been applied for the ambient air quality monitoring. Since PSD does not apply to the

Rosemont project, strict adherence to the PSD rules is not a regulatory requirement. It is important to

note that while the PSD regulations provide a framework for ambient air quality monitoring and for

dispersion modeling, PSD only applies to sources with a potential to emit that is much greater than

those from the Rosemont project.

Based on the potential to emit (PTE) for all criteria pollutants, Rosemont will be categorized as a

synthetic minor stationary source. Emissions of hazardous air pollutants (HAPs) will not exceed the

major source thresholds of 10 tpy for a single HAP or 25 tpy for all HAPs combined, therefore

Rosemont will also be a minor source of HAP emissions. Since the PTE for all criteria pollutants will

be below 100 tpy and the facility will not be a major source of HAPs, Rosemont will not be subject to

Title V permitting. Consequently, the facility will operate under a Class II Permit issued by the

Arizona Department of Environmental Quality (ADEQ). Ambient air quality monitoring and air

http://www.epa.gov/air/criteria.html

http://www.epa.gov/oaqps001/greenbk/


dispersion modeling are not routinely required of Class II sources. Since the PSD status of the

project was not yet determined early in the project’s planning stages, ambient air quality monitoring

was initiated as if PSD would apply. At the request of the Forest Service to identify the potential

impacts of emissions from Rosemont on air quality, dispersion modeling will be performed.

2.3 Air Quality Regulatory Authority

Rosemont will be located within the Pima Intrastate Air Quality Control Region (AQCR) which

encompasses Pima County. The Pima County Department of Environmental Quality (PCDEQ)

permits and regulates most stationary sources of emissions located within their jurisdiction although

ADEQ retains original jurisdiction over some types of sources as provided in §49-402(B) of the

Arizona Revised Statutes (ARS). An application for an air quality permit was initially submitted to

PCDEQ. However, the existing Pima County State Implementation Plan is inconsistent with state law

in that it further grants jurisdiction of certain other sources (such as Rosemont) to ADEQ. As a result,

while state law would indicate that PDEQ is the appropriate air permitting authority for Rosemont, the

Pima County SIP requires otherwise. The PCDEQ has denied the issuance of an air quality permit

and Rosemont has therefore submitted an application for a Class II air permit to ADEQ.


3. DISPERSION MODELING INPUT DATA AND DEFAULTS

The dispersion modeling will be conducted using the PSD regulatory guideline dispersion model

developed by the EPA in conjunction with the American Meteorological Society (however, as

previously stated, Rosemont is not subject to PSD requirements). The model is called the AMS/EPA

Regulatory Model, or AERMOD. Evaluation of the maximum ambient air quality impacts from the

proposed Rosemont Project will be conducted using the latest version of AERMOD (User’s Guide for

the AMS/EPA Regulatory Model – AERMOD, U.S. Environmental Protection Agency, Office of Air

Quality Planning and Standards, Emissions, Monitoring, and Analysis Division, Research Triangle

Park, North Carolina, EPA-454/B-03-001, September 2004), version 12060. JBR Environmental

Consultants, Inc. (JBR) uses the commercial version of AERMOD from BEE-Line Software (P.O. Box

7348, Asheville, NC 28802, (828) 628-0636). Since the Saguaro East National Forest lies within 50

KM of the proposed Rosemont Project, the Forest Service also recommended using AERMOD to

evaluate the ambient air quality impacts at this Class I area based on the FLAG 2010 guidance.

EPA’s Guideline on Air Quality Models addresses the regulatory application of air quality models for

assessing criteria pollutants under the Clean Air Act1. Appendix A of the Guideline identifies

AERMOD as the preferred model for a wide range of regulatory applications. The AERMOD

modeling system consists of one main program (AERMOD) and two pre-processors (AERMET and

AERMAP). The major purpose of AERMET is to calculate boundary layer parameters for use by

AERMOD. The major purpose of AERMAP is to calculate terrain heights and receptor grids for

AERMOD. Both AERMET and AERMAP require observational data to parameterize the growth and

structure of the atmospheric boundary layer. AERMOD uses terrain, boundary layer and source data

to model pollutant transport and dispersion for calculating temporally averaged air pollution

concentrations.

AERMOD's three models and required model inputs are as follows:

1) AERMET: calculates boundary layer parameters for input to AERMOD

a. Model inputs: wind speed; wind direction; cloud cover; ambient temperature;

morning sounding; albedo; surface roughness; Bowen ratio

b. Model outputs for AERMOD: wind speed; wind direction; ambient temperature;

lateral turbulence; vertical turbulence; Sensible heat flux; friction velocity;

Monin-Obukhov Length

2) AERMAP: calculates terrain heights and receptor grids for input to AERMOD

a. Model inputs: DEM data [x,y,z]; design of receptor grid (pol., cart., disc.)

b. Model outputs for AERMOD: [x,y,z] and hill height scale for each receptor

3) AERMOD: calculates temporally-averaged air pollution concentrations at receptor

locations for comparison to the NAAQS

1 “Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain)

Dispersion Model and Other Revisions: Summary (Final Rule).” Federal Register 70:216 (9 November 2005) p. 68218


a. Model inputs: source parameters (from permit application); boundary layer

meteorology (from AERMET); receptor data (from AERMAP)

b. Model outputs: temporally averaged air pollutant concentrations

3.1 Recommended Regulatory Default Options

The following recommended regulatory default options for AERMOD as stated in the Guideline will be

used for the model runs: stack-tip downwash, incorporation of the effects of elevated terrain, and

calms and missing data processing routines.

3.2 Missing Data Processing Routines

The missing data processing routines that are included in AERMOD allow the model to handle

missing meteorological data in the processing of short term averages. The model treats missing

meteorological data in the same way as the calms processing routine (i.e., it sets the concentration

values to zero for that hour and calculates the short term averages according to EPA's calms policy,

as set forth in the Guideline). Calms and missing values are tracked separately for the purpose of

flagging the short term averages. An average that includes a calm hour is flagged with a 'c', an

average that includes a missing hour is flagged with an 'm', and an average that includes both calm

and missing hours is flagged with a 'b'. If the number of hours of missing meteorological data

exceeds 10 percent of the total number of hours for a given model run, a cautionary message is

written to the main output file, and the user is referred to Section 5.3.2 of On-site Meteorological

Program Guidance for Regulatory Modeling Applications (EPA, 1987).

3.3 Regional Topography

Rosemont will be located in the Santa Rita Mountains which trend northeast to southwest with

elevations ranging from 4,500 feet to over 6,000 feet (See Section 1.5). To the west of the mountain

range lies the broad Santa Cruz River Valley and to the east of the mountains lies a smaller valley

bisected by Cienega Creek.

3.4 Rural/Urban Classification

For modeling purposes, the rural/urban classification of an area is determined by either the

dominance of a specific land use or by population data in the study area. Generally, if the sum of

heavy industrial, light-moderate industrial, commercial, and compact residential (single and multiple

family) land uses within a three kilometer radius from the facility are greater than 50%, the area is

classified as urban. Conversely, if the sum of common residential, estate residential, metropolitan

natural, agricultural rural, undeveloped (grasses), undeveloped (heavily wooded) and water surfaces

land uses within a three kilometer radius from the facility are greater than 50%, the area is classified

as rural. Alternatively, if the population is greater than 750 persons per km2, the area is also

classified as urban.

As shown in the aerial photograph in and the topographic map in Figures 1.1 and 5.1, rural land use

in the area surrounding the proposed Rosemont Project is much greater than 50%. Thus, the rural

classification will be used in the modeling.


3.5 Regional Climatology

The climate of the Rosemont area is semi-arid with precipitation varying with elevation and season.

The 30-year normal (1971 to 2000) annual average precipitation for the Santa Rita Experimental

Range station is 23.41 inches (Western Regional Climate Center). Over this 30-year period, nearly

half of the precipitation occurred in the months associated with the Arizona monsoon season

comprised of July, August and September. The least amount of precipitation occurred during the

months of April, May and June.

Temperatures regionally are moderate to extreme with maximums and minimums also varying with

elevation. The 30-year normal average monthly maximum temperatures at the Santa Rita

Experimental Range station ranged from a low of 60.4°F in January to a high of 93.3°F in June.

Average monthly minimum temperatures ranged from a low of 37.5°F in December and January to a

high of 66.8°F in July.

On-site meteorological monitoring was performed to obtain site-specific temperature and wind data as

described in further detail in Section 3.6.

3.6 Meteorological Monitoring for On-Site Data

On-site meteorological monitoring was initiated by Rosemont in April 2006 and is continuing to date.

Complete quarterly data summary and semi-annual audit reports have been submitted to the PCDEQ

and ADEQ since the monitoring began. Detailed results of the monitoring program can be found in

these quarterly reports. On-site monitoring was performed in accordance with EPA’s Meteorological

Monitoring Guidance for Regulatory Modeling Applications.

The modeling will be based upon the on-site weather observations from the Rosemont monitoring

site, which is located at the center of the proposed open pit at an elevation of 5,350 feet as shown in

Figure 5.1 . Parameters measured at the Rosemont monitoring site include ambient temperature at

2 meters, differential temperature between 2 and 10 meters, and wind speed and wind direction at 10

meters. The monitoring site was chosen following EPA’s Meteorological Monitoring Guidance for

Regulatory Modeling Applications (EPA-454/R-99-005, February 2000). A monitoring protocol

entitled Monitoring Protocol and Quality Assurance Project Plan for Conducting Ambient PM10 and

Meteorological Monitoring for the Proposed Rosemont Copper Mine Pima County, Arizona (July 1,

2006) (Monitoring Protocol and QAAP) was submitted to PCDEQ and is available at

http://www.rosemonteis.us/documents/013220. Quarterly reports of the meteorological

measurements were subsequently submitted to both PCDEQ and to ADEQ.

As stated above, monitoring began in April 2006 and is on-going. The database, however, is not

continuous as data between December 2006 and February 2007 were lost due to a data logger

malfunction (see quarterly and audit reports submitted to the PCDEQ and ADEQ). The modeling will

be conducted based upon 3 full years of on-site data, with missing data periods filled in with data from

other years for the same time period. Wind roses for the data collected in 2006-2007, 2007-2008 and

2008-2009 are presented in Figures 3.2 through 3.4, respectively. The year-to-year consistency in

the wind data indicates that meteorological data collection was consistent. The missing data for

December 2006 to February 2007 was filled in with data for the same period from the next year.

http://www.rosemonteis.us/documents/013220


3.7 Meteorological Data Processing for AERMOD

Meteorological data will be combined into AERMOD-ready surface and upper air input files using

AERMET. As a regulatory component of the AERMOD modeling system, the AERMET program

serves as the meteorological preprocessor for AERMOD. AERMET is designed to combine and

quality control on-site and NWS surface and upper air data for use by AERMOD.

3.8 Sky Cover Data

AERMOD requires parameters for determining boundary layer conditions which include opaque sky

cover (or total sky cover). The Rosemont on-site surface measurements do not include sky cover

data. Per EPA’s AERMET guidance, the concurrent sky cover data for the on-site surface

meteorological data will be obtained from the nearest NWS site, the Tucson Airport (WBAN 23160).

3.9 Upper Air and Surface Meteorological Data

AERMOD also requires upper air data. Only two upper air sites are available for Arizona, Tucson and

Flagstaff. The only other nearby upper air data is at Santa Rita, New Mexico, which is approximately

150 miles away from the Rosemont site. Upper air data concurrent with the on-site surface

meteorological data will be obtained from the NWS Tucson Airport station (WBAN 23160), which is

the closest NWS station.

3.10 Surface Characteristics

Surface conditions at the measurement site, referred to as the surface characteristics, influence the

boundary layer parameter estimates generated by AERMOD. Obstacles to the wind flow, the amount

of moisture at the surface, and reflectivity of the surface all affect the boundary layer estimates.

These influences are quantified through the surface albedo, Bowen ratio and roughness length, and

are introduced into AERMOD through the files generated by AERMET.

The albedo is the fraction of total incident solar radiation reflected by the surface back to space

without absorption. Typical values range from 0.1 for thick deciduous forests to 0.90 for fresh snow.

The daytime Bowen ratio, an indicator of surface moisture, is the ratio of the sensible heat flux to the

latent heat flux and is used for determining planetary boundary layer parameters for convective

conditions. While the diurnal variation of the Bowen ratio may be significant, the Bowen ratio usually

attains a fairly constant value during the day. Midday values of the Bowen ratio range from 0.1 over

water to 10.0 over desert. The surface roughness length is related to the height of obstacles to the

wind flow and is, in principle, the height at which the mean horizontal wind speed is zero. Values

range from less than 0.001 m over a calm water surface to 1 m or more over a forest or urban area.

The values for surface albedo, Bowen ratio and roughness length can be entered into the AERMET

preprocessor based on frequency and sector. The frequency defines how often these characteristics

change, or alternatively, the period of time over which these characteristics remain constant.

The frequency defines how often these characteristics change, or alternatively, the period of time

over which these characteristics remain constant. The frequency can be annual, seasonal (winter

[December, January, February], spring [March, April, May], summer [June, July, August], fall


[September, October, November]), or monthly, corresponding to 1, 4, or 12 periods, respectively.

Sectors refer to the number of non-overlapping sectors into which the 360° compass is divided.

A minimum of 1 and a maximum of 12 sectors can be specified (i.e., 1 sector of 360°, up to 12 non-

overlapping sectors of 30°). Thus, AERMET allows the values for surface albedo, Bowen ratio and

roughness length to be entered annually, seasonally or monthly for each sector, the number of which

can range between 1 and 12. As shown in the Monitoring Protocol and QAAP, the area surrounding

the proposed Rosemont Project is undeveloped, pinyon-juniper mountainous terrain in all directions.

Consequently, surface characteristics will be entered for a single sector.

The EPA has developed a computer program called AERSURFACE to aid users in obtaining realistic

and reproducible surface characteristic values for the albedo, Bowen ratio, and surface roughness

length for input to AERMET. The program uses publicly available national land cover datasets and

look-up tables of surface characteristics that vary by land cover type and season. Land cover data

(not partitioned) from the USGS NLCD92 will be used for the modeling as recommended by the

AERSURFACE user guide (http://www.epa.gov/ttn/scram/7thconf/aermod/aersurface_userguide.pdf).

The surface characteristics that will be used in the modeling will be entered on a seasonal basis and

are listed in Table 3.1. The values listed in Table 3.1 were generated by AERSURFACE.

Table 3.1 Surface Characteristics Proposed for Use in the AERMOD Modeling

Surface Characteristic

* Spring Summer Autumn Winter

Albedo

0.25 0.25 0.25 0.25

Bowen Ratio

2.88 3.76 5.70 5.70

Surface

Roughness 0.153 0.153 0.153 0.152

* Generated by AERSURFACE, dated 0809

Center UTM Easting (meters): 522896.0; Center UTM Northing (meters): 3521802.0; UTM Zone: 12, Datum: NAD83

Study radius (km) for surface roughness: 1.0

Airport? N, Continuous snow cover? N

Surface moisture? Average, Arid region? Y, Month/Season assignments? Default

Late autumn after frost and harvest, or winter with no snow: 12 1 2

Winter with continuous snow on the ground: 0

Transitional spring (partial green coverage, short annuals): 3 4 5

Midsummer with lush vegetation: 6 7 8; Autumn with un-harvested cropland: 9 10 11

http://www.epa.gov/ttn/scram/7thconf/aermod/aersurface_userguide.pdf


Figure 3.1 Topographic Map Showing Location of the PM10 and Meteorological Monitoring Sites.

Meteorological Monitoring Site

Rosemont Mine Open Pit Area

PM10 Monitoring Site


WRPLOT View - Lakes Environmental Software

WIND ROSE PLOT:

Rosemont Wind Rose Plot 2nd Quarter 06 - 1st Quarter 07

COMMENTS: COMPANY NAME:

Augusta Resources - Rosemont Project

DATE:

6/19/2009

PROJECT NO.:

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED

(m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 0.14%

TOTAL COUNT:

8749 hrs.

CALM WINDS:

0.14%

AVG. WIND SPEED:

2.76 m/s

DISPLAY:

Wind Speed

Figure 3.2 Wind Rose for the Rosemont Meteorological Station for the Time Period April 1, 2006 – March 31, 2007.



WIND ROSE PLOT:




DATE:

6/19/2009

PROJECT NO.:

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED

(m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 0.43%

TOTAL COUNT:

8766 hrs.

CALM WINDS:

0.43%

AVG. WIND SPEED:

2.78 m/s

DISPLAY:

Wind Speed

Figure 3.3 Wind Rose for the Rosemont Meteorological Station for the Time Period April 1, 2007 - March 31, 2008.



WIND ROSE PLOT:




DATE:

6/19/2009

PROJECT NO.:

NORTH

SOUTH

WEST EAST

4%

8%

12%

16%

20%

WIND SPEED

(m/s)

>= 11.1

8.8 - 11.1

5.7 - 8.8

3.6 - 5.7

2.1 - 3.6

0.5 - 2.1

Calms: 0.89%

TOTAL COUNT:

8753 hrs.

CALM WINDS:

0.89%

AVG. WIND SPEED:

2.71 m/s

DISPLAY:

Wind Speed

Figure 3.4 Wind Rose for the Rosemont Meteorological Station for the Time Period April 1, 2008 - March 31, 2009.


4. BACKGROUND CONCENTRATIONS

To evaluate the potential impacts of emissions from Rosemont operations on the public, the

dispersion modeling evaluation must consider the existing background concentrations of pollutants in

the area where impacts are being evaluated. The background concentration of a given pollutant is

added to the modeled impact from Rosemont operations, and the result is compared to the NAAQS

for that pollutant.

For the DEIS, potential air quality impacts are being evaluated near the Rosemont location as well as

at the Class I areas located within 50 km of the project site. Different background concentrations will

be used to represent local conditions for each of the locations where impact is being evaluated. For

example, to evaluate the impacts at Saguaro National Park East, the background concentrations at

Saguaro National Park East will be used. These background concentrations will be obtained from

ambient air quality monitoring that is done at that location. However, to evaluate impacts near the

fenceline and within close proximity to the Rosemont site, different pollutant background

concentrations will be used. The selected background concentrations for each pollutant at Rosemont

will be chosen in order to best represent existing background pollutant concentrations at the site since

no on-site data exists for pollutants other than PM10.

Pollutants directly emitted by operations at Rosemont and under evaluation for dispersion modeling

purposes are PM10, PM2.5, NO2, CO, and SO2. Thorough evaluations of available air quality data are

required in order to determine what background concentration is most representative of conditions at

a particular location. Modeling guidance used for PSD permitting provides a general methodology for

choosing background concentrations when on-site or nearby data are not available. However, due to

site- and location-specific conditions and the limited representative monitoring data that is available,

there is no one approach that is required to be used, even for purposes of modeling for PSD

permitting. For purposes of this modeling protocol, available guidance will be followed and proposed

variations from guidance documents will be identified.

4.1 PM10

Rosemont initiated pre-application air quality monitoring for PM10 in June 2006. At that time it was

undetermined whether the project would trigger PSD permitting for PM10, so the on-site monitoring

was performed in compliance with the PSD regulations. The monitoring ended in June 2009. The

location of the monitoring site is shown in the Monitoring Protocol and QAAP. Complete quarterly

data summary and audit reports have been submitted to the PCDEQ and ADEQ since the monitoring

began. Detailed results of the monitoring program can be found in these quarterly reports and the

quarterly summaries are presented in Appendix B. The on-site PM10 data will be used to define

background concentrations for locations near Rosemont.

As stated in the November 9, 2005 Revision to the Air Quality Models (found at 40 CFR 51 Appendix

W) that is applied to PSD permitting and can be followed in this case as a guideline for non-PSD

modeling, the 24-hr PM10 background concentration will be based on the average of the highest 24-hr

concentrations recorded for each year. With respect to determination of this value, ambient PM10

monitoring commenced at the start of the 3rd

quarter of 2006. Annual time periods are thus defined


as the time period from July of one year through June of the following year. A listing of the highest

and second highest concentrations for the three year period is tabulated in Table 4.1.

Table 4.1 PM10 Monitoring Results

Year

Highest Concentration

(g/m3)

2nd Highest Concentration

(g/m3)

July 2006 -June 2007 71.3 27.0

July 2007 -June 2008 40.3 28.2

July 2008 - June 2009 31.6 21.2

The high concentration of 71.3 g/m3 was recorded on the second day of the monitoring program and

is not representative of the typical background concentration observed at the Rosemont site.

Likewise, the measured ambient PM10 concentration of 40.3 g/m3 appears to be abnormally high in

comparison to other monitored concentrations.

Statistical analyses were performed to quantitatively evaluate whether these data points are

legitimate and should be included in the calculation of the PM10 background concentration. The

statistical analyses provided in Appendix C show that the monitored concentration of 71.3 g/m3 is an

extreme outlier and the second highest monitored concentration of 40.3 g/m3 is an outlier.

Additional qualitative analysis has been performed to evaluate whether the high data point generally

meets the criteria required of an “exceptional event” under 40 CFR Part 50 §§50.1(j), (k), and (l), and

50.14, Treatment of air quality monitoring data influenced by exceptional events. The rule guiding the

determination of whether a high monitored value is an exceptional event applies to air quality

regulatory agencies that are seeking to determine an area’s NAAQS attainment or nonattainment

status. It does not apply to individual sources or on-site monitoring. However, since the Rosemont

project is also not subject to monitoring or modeling under PSD, the exceptional event evaluation

process used by States does generally provide a reasonable framework for evaluating whether the

high monitored value at the Rosemont site should be included in determining the PM10 background

concentration.

Per 40 CFR Part 50 and the Preamble to the Final Rule at 72 FR 13560, an exceptional event:

(i) Affects air quality as established by an air quality impact that:

1. Falls above the level of the applicable standard; and

2. Is significantly beyond the normal fluctuating range of air quality, including

background air quality concentrations, and

3. Should be large enough that without it there would have been no exceedances

(ii) Is an event that is not reasonably controllable or preventable;

(iii) Is an event caused by human activity that is unlikely to recur at a particular location or a

natural event; and is


(iv) Determined by EPA to be an exceptional event

While the above criteria clearly apply to States that are using monitored data to evaluate attainment

status and not to sources, all available information regarding the monitored concentration of 71.3

g/m3

indicates that it should be excluded. Since dispersion modeling is being performed to evaluate

the project’s anticipated effects on ambient air quality as determined through comparison with the

NAAQS, a similar approach for excluding a high data point due to an exceptional event is appropriate

in this case.

As described in Appendix K of 40 CFR Part 50—Interpretation of the National Ambient Air Quality

Standards for Particulate Matter, a State or other air quality jurisdiction that is evaluating the

monitored concentrations of PM10 to determine whether the area is or is not in compliance with the

NAAQS can exclude high data points under certain circumstances. This is because the Clean Air Act

and EPA recognize that including in the computation of exceedances or averages a high value that is

due to an exceptional event could result in inappropriate estimates of expected annual values.

Including high values that are very unlikely to recur could place an area in nonattainment for reasons

over which it has no control and for which regulatory control measures would have essentially no

effect.

The Clean Air Act states that air quality data should be carefully screened to ensure that events not

likely to recur are represented accurately in all monitoring data and analyses (42 U.S.C.

7619(b)(3)(A)). Based on the above considerations, the monitored concentration of 71.3 g/m3 will

not be used. Instead, the next highest monitored concentration that occurred during the monitoring

period, 40.3 g/m3, will be used in its place. This is a conservative value, because as shown in Table

4.1, the second highest PM10 concentration recorded during the first year of monitoring is 27.0 µg/m3.

The background concentration for evaluating predicted impacts with the 24-hour PM10 NAAQS will be

the mean of the highest values in Table 4.1 with 71.3 g/m3 replaced by 40.3 g/m

3.

The annual PM10 background concentration for the area near Rosemont will be based on the average

of the annual averages for the three-year monitoring period. This is the methodology presented in the

November 9, 2005 Revision to the Air Quality Models (40 CFR 51, Appendix W) applied to PSD

permitting and can be followed in this case as a guideline for Rosemont’s non-PSD modeling. The

summary of the annual averages and the resulting annual PM10 background concentration at

Rosemont of 11.7 g/m3 is presented in Table 4.2.

Background concentrations for the impact analysis at the Saguaro East NP will be based on the

2007-2009 aerosol data from the Saguaro East NP IMPROVE monitoring site. The 24-hr and annual

average background PM10 concentrations at Saguaro East NP are 47.6 g/m3

and 12.6 g/m3,

respectively.


Table 4.2 PM10 Annual Average Monitored Concentrations

Year Average Monitored PM10

Concentration (µg/m

3)

July 2006 -June 2007 12.3

July 2007 -June 2008 12.4

July 2008 - June 2009 10.3

Average of Annual Averages 11.7

4.2 PM2.5

To obtain a representative background concentration for PM2.5 in the vicinity of Rosemont, a number

of monitoring sites were evaluated to determine which site most closely reflected the conditions near

Rosemont. The closest PM2.5 monitors—located by straight-line distance from Rosemont—are in the

Tucson metropolitan area.

The Rosemont site is located in the Santa Rita Mountains which trend northeast to southwest.

Elevations range from 4,500 feet to over 6,000 feet and feature complex terrain. The approximate

elevation of the Rosemont site itself is 5350 feet. The nearest sources of emissions are the small

community of Green Valley located 15 miles to the west of Rosemont and several industrial facilities

located 3 to 8 miles further west. These sources are distant from the Rosemont site and on the other

side of the Santa Rita Mountains. Since Rosemont is not located near any existing monitors, nor are

the locations of the existing Pima County monitors representative of the Rosemont site due to the

significant urban influences that are nonexistent near Rosemont, it is necessary to evaluate other

monitors to obtain background data for evaluating the fence-line impacts.

Two EPA IMPROVE monitoring sites, the Saguaro National Monument (NM) site and Chiricahua

National Monument (NM) site, were evaluated for background data for the PM2.5 modeling analysis.

The Saguaro NM monitor is at an elevation of 3080 ft and located in close proximity to the Tucson

metropolitan area, and thus influenced by urban and industrial emissions from Tucson. The

Chiricahua NM monitor located in Cochise County is at an elevation of 5150 feet, similar to that of

Rosemont, and is surrounded by similar complex terrain features like the Rosemont site. The

physical characteristics of the terrain near the Chiricahua National Monument monitoring site are

significantly more representative of the terrain in the vicinity of the Rosemont site compared to the

Saguaro National Park East terrain characteristics. Emission sources impacting the Chiricahua

National Monument monitoring site are more closely representative of the sources impacting the

Rosemont site than the sources impacting the Saguaro National Park East monitoring site. Thus the

Chiricahua NM site was selected for background data, as it is more representative of the Rosemont

site on account of its similar terrain features, elevation and remoteness from emission sources.

EPA published a memorandum titled, "Modeling Procedures for Demonstrating Compliance with

PM2.5 NAAQS" on March 23, 2010 that states the following: "The representative monitored PM2.5

design value, rather than the overall maximum monitored background concentration, should be used

as a component of the cumulative analysis. The PM2.5 design value for the annual averaging period


is based on the 3-year average of the annual average PM2.5 concentrations; for the 24-hour averaging

period, the design value is based on the 3-year average of the 98th percentile 24-hour average PM2.5

concentrations for the daily standard. Details regarding the determination of the 98th percentile

monitored 24-hour value based on the number of days sampled during the year are provided in the

ambient monitoring regulations, Appendix N to 40 CFR Part 50."

As described in the document entitled, "Revised AERMOD Modeling Report to Assess Ambient Air

Quality Impacts" dated April 4, 2011, the procedure detailed in Appendix N of 40 CFR Part 50 will be

applied to arrive at the monitored background PM2.5 design values for the Saguaro NM IMPROVE site

(based on 2008-2010 monitored data) for evaluating impacts at that location, and Chiricahua NM

IMPROVE site (based on 2008-2010 monitored data) will be used to evaluate impacts in the vicinity

of Rosemont.

4.3 NO2

Emissions from Rosemont operations will include tailpipe emissions from mobile equipment

conducting mining operations in addition to minor fuel combustion sources used in ore processing

operations. Tailpipe emissions from mobile sources are not considered in applications for air quality

permits, but those emissions are included in air impact analyses for Environmental Impact

Statements. The planned air impact analysis will consider emissions from both process sources and

mobile sources. Tail pipe emissions are generally comprised primarily of both NOx and CO.

Nitrogen dioxide (NO2) is formed by the oxidation of nitric oxide (NO) which is a byproduct of

combustion. Ambient NO2 concentrations for locations in Arizona are currently monitored only in

urban areas and near coal fired power plants. One rural monitoring site where emissions are due to

minor vehicle traffic and outboard motorboats on Alamo Lake was in place during 2005–2006. There

are no monitoring sites in the vicinity of the proposed Rosemont project. Table 4.3 provides the NO2

monitoring station locations in Arizona and the maximum one-hour average NO2 concentrations

monitored from 2005 through 2008 as reported by ADEQ in the Air Quality Annual Reports (available

at http://www.azdeq.gov/function/forms/reports.html).

Urban areas are very highly influenced by emissions from vehicle traffic that do not exist near

Rosemont. As a result, those monitoring locations are not representative of existing NO2 background

concentrations near Rosemont. Coal-fired power plants are significant sources of NO2 and

monitoring data from those locations do not represent existing background NO2 concentrations near

Rosemont. The only remaining monitor in Arizona that could be considered representative of NO2

background concentrations for Rosemont is located at Alamo Lake.

The Rosemont site is similar to the Alamo Lake site in that the only sources of NO2 are minor vehicle

traffic on a road approximately 2.5 miles from the site. Both locations are rural. The highest

background 1-hr NO2 concentration recorded at the Alamo Lake site measured during a two year

monitoring program (2005-2006) was 24.5 g/m3 (note that the ADEQ Air Quality Annual Reports

show NO2 concentrations in ppm, not µg/m3). The second-highest monitored 1-hour concentration

was 20.7 g/m3. For purposes of providing a background NO2 concentration at the Rosemont site,

the highest of the two years, 24.5 g/m3 will be used as the 1-hr background NO2 concentration. In

the absence of on-site NO2 data, this value will also be used for the Saguaro East NP. Use of the

http://www.azdeq.gov/function/forms/reports.html


highest monitored Alamo Lake 1-hour NO2 value represents a conservative estimate of NO2

background.

Table 4.3 Maximum Monitored Value of the One-Hour Average NO2 Concentration

(ppm)

Monitoring Station Location Type 2005 2006 2007 2008

Apache County

TEP – Springerville – Coyote Hills

Coal-fired power plant

0.014 0.018 0.037 0.025

La Paz County

Alamo Lakea Rural 0.011 0.013 - -

Maricopa County

Buckeye Urban 0.053 0.047 0.069 0.059

Central Phoenix Urban 0.095 0.085 0.077 0.076

Greenwood Urban 0.131 0.111 0.094 0.138

JLG Supersiteb Urban 0.077 0.067 0.076 0.073

South Scottsdale Urban 0.079 0.065 0.068 0.063

West Phoenix Urban 0.100 0.092 0.082 0.065

Pima County

22nd St. & Craycroft - Tucson

Urban 0.056 0.051 0.058 0.054

Childrens Park - Tucson Urban 0.049 0.054 0.049 0.049

Yuma County

Yuma Game & Fishc Urban 0.051 0.067 0.060 -

a Seasonal Monitor – operated May 20 – December 31, 2005 and April – October, 2006.

b Seasonal Monitor – operated January 1 – April 30 and October 1 – December 1, 2008.

c Seasonal Monitor – operated April 1, 2005 – April 12, 2007.

Rosemont has recently been made aware of a NO2 background concentration data set from a monitor

located at Tonto National Forrest. The duration of this database is from May 2002 to Nov 2006. This

data set is available from the EPA Air Quality Systems database and is currently being evaluated. If

the Tonto National Forrest monitor data is selected, then the 98th percentile of the annual distribution

of daily maximum 1-hour NO2 concentration values averaged across 3 years of the monitored data

will be used as background.

4.4 CO

CO is produced by the incomplete combustion of fuels with anthropogenic activities (automobiles,

construction equipment, lawn and garden equipment, commercial and residential heating, etc.)

representing the major source of emissions. Consequently, the CO monitoring sites in Arizona are

located exclusively in urban areas (Phoenix, Tucson and Casa Grande) and there are no

representative monitoring stations to determine background CO concentrations.


The ADEQ recommended CO background concentrations for rural areas with no major sources of CO

for both the 1-hour and 8-hour averaging periods are 582 g/m3 (communications with the ADEQ see

Appendix E). These values will be used as background CO concentrations for all impact analyses:

the fence line, near vicinity and Saguaro East NP impact analysis.

4.5 SO2

Historically, the principal sources of SO2 emissions in Arizona have been copper smelters and coal-

fired power plants. The non-urban SO2 monitoring sites in Arizona are located in areas near

smelters, including Miami, Globe, and Hayden, and near coal-fired power plants, including

Springerville, Page, and Bullhead City. To evaluate whether SO2 is a pollutant of concern for large

populations of people in Arizona, SO2 monitors are also located in the urban areas of Phoenix and

Tucson. Since the Rosemont site is neither near a copper smelter or coal-fired power plant, nor

located near an urban area, there are no representative monitoring stations to determine background

SO2 concentrations.

The ADEQ-recommended SO2 background concentrations for rural areas with no major sources of

SO2 for the 3-hour, 24-hour and annual averaging periods are 43 g/m3, 17 g/m

3 and 3 g/m

3,

respectively (communications with the ADEQ; see Appendix E). These values will be used as

background SO2 concentrations for the fence line and near vicinity impact analyses as well as for the

Saguaro East NP impact analysis.

4.6 Pollutant Transport to Rosemont

Transport of emissions to the Rosemont site from Tucson and Interstate 10 (I-10) to effect

background concentrations at the site is highly unlikely as illustrated by wind roses for the Rosemont

site (Figures 3.2 through 3.4) and for the Tucson airport (Figure 4.1). Rosemont wind patterns have a

very strong western component with almost no northerly component. This is primarily due to the

site’s location on the eastern slope of the Santa Rita mountains, which extend to the north-northeast

and to the south of the site.

Any emissions transported toward Rosemont from the north or northwest, such as from the Tucson

metropolitan area, would have to travel over or around the higher elevations in those directions. Any

emissions transported toward Rosemont from the east would have to overcome the frequent,

relatively strong winds from the west. The wind rose from the Tucson airport as shown in Figure 4.1

exhibits a pronounced southeasterly component directing emissions away from the site. The primary

Tucson winds and their accompanying emissions tend to blow away from the Rosemont location,

which is at a distance of approximately 30 miles and over elevations greater than those at Rosemont.

The Rosemont location is approximately 15 miles south of Interstate 10 (I-10), which runs primarily

from east to west. The interstate changes direction at a point almost due north of Rosemont and

begins heading northwest. As indicated in Figures 3.2 through 3.4, the frequency of winds from the

direction of I-10 to the Rosemont site is less than 5%. Vehicle emissions could potentially be

transported from I-10 to the Rosemont site. However, the winds at Rosemont only rarely blow from

the north or northeast. The relative frequency and strength with which the winds at Rosemont blow

from the west make it highly unlikely that vehicle emissions originating at I-10 have an impact at


Rosemont that is more significant than what the proposed background concentrations already

consider.

Figure 4.1 Wind Rose for the Tucson Airport

Aside from the very loaw frequency of winds from the Tucson and I-10 areas to Rosemont, it should

also be noted that the highest impacts from a stationary source usually occur under stable

meteorological conditions. Such conditions generally occur during calm conditions characterized by

down slope winds from elevated terrain to lower elevations. Upslope winds from low elevations to


higher elevations generally occur during less stable conditions that produce lower impacts. The

Rosemont site is in complex terrain at a higher elevation than both Tucson and the I-10.

Consequently any emissions that could be transported from these areas to Rosemont would be

greatly dispersed and would occur when Rosemont impacts are at reduced levels. Including

additional emissions in the background concentrations during meteorological conditions when

Rosemont emissions produce peak impacts would inappropriately skew the modeling. Therefore,

while it is theoretically possible that transported emissions could reach Rosemont, there is no

indication that use of the proposed methods to determine background pollutant concentrations for

modeling, which are based on the regulatory methods, are insufficient.


5. MODELING ANALYSIS DESIGN

5.1 Ozone Limiting Method for Evaluating NO2 Impacts

The Ozone Limiting method (OLM), which is a non-regulatory option in AERMOD, will be used to

evaluate the impact of NO2 in the near vicinity of the Rosemont Project as well as at the Saguaro East

National Park. Background ozone data for Chiricahua National Monument will be used for the

Rosemont near-vicinity impact evaluation.

OLM involves an initial comparison of the estimated maximum NOx concentration and the ambient

ozone concentration to determine the limiting factor in the formation of NO2. If the ozone

concentration is greater than the maximum NOx concentration, total conversion is assumed. If the

NOx concentration is greater than the ozone concentration, the formation of NO2 is limited by the

ambient ozone concentration. The method also uses a correction factor to account for in-stack

conversion of NOx to NO2. While the modeling being performed for Rosemont is not subject to the

regulatory requirements therein, the use of OLM for the Rosemont modeling analysis is based on the

requirements of 40 CFR Part 51, Appendix W, 3.2.2(e) being met as follows:

3.2.2(e)(i). The model has received scientific peer review;

The chemistry for the OLM option has received peer review as noted in "Sensitivity Analysis

of PVMRM and OLM in AERMOD" document posted in EPA's SCRAM website. The

document indicates that the model appears to performs as expected.

3.2.2(e)(ii). The model can be demonstrated to be applicable to the problem on a theoretical basis;

The model has been reviewed and the chemistry has been widely accepted by the EPA and

other government agencies as being appropriate for addressing the formation of NO2 and the

calculation of NO2 concentration at receptors downwind. For a given concentration of NOx

emission rate and ambient ozone concentration, the NO2/NOx conversion ratio for OLM is

primarily controlled by the ground level NOx concentrations.

The EPA issued a memorandum dated March 1, 2011 entitled "Additional Clarification

Regarding Application of Appendix W Modeling Guidance for 1-hr NO2 NAAQS." This memo

indicates that the PVMRM method as currently implemented may have a tendency to

overestimate the conversion of NO to NO2 for low-level plumes by overestimating the amount

of ozone available for the conversion due to the manner in which the plume volume is

calculated. Furthermore, the EPA's Risk and Exposure Assessment (REA) for the most

recent NO2 NAAQS review (EPA, 2008) for the Atlanta area used the OLM option with

OLMGROUP ALL to estimate NO2 concentration from mobile source emissions. The vast

majority of the NO2 emissions at the Rosemont facility will be from mobile sources.

Additionally, the "Sensitivity Analysis of PVMRM and OLM in AERMOD" report indicates that

PVMRM/OLM provides a better estimation of the NO2 impacts compared to other screening

options.

3.2.2(e)(iii). The data bases which are necessary to perform the analysis are available and adequate;


Hourly background ozone data for the period April 2006 to March 2009 from the Chiricahua

National Monument IMPROVE site will be used. The Chiricahua NM site is the most

representative of the terrain and conditions at the Rosemont site.

3.2.2(e)(iv). Appropriate performance evaluations of the model have shown that the model is not

biased towards underestimates;

Although no assessment of bias has been conducted for the OLM model, based on the

"Sensitivity Analysis of PVMRM and OLM in AERMOD" report, OLM was estimated to provide

similar or more conservative estimates of concentration than PVMRM and therefore would

also be judged to be unbiased toward underestimation.

3.2.2(e)(v). A protocol on methods and procedures to be followed has been established;

The methods and procedures for conducting an assessment for determining compliance with

the 1-hr federal NAAQS are contained in other sections of this document.

EPAs guidance “Additional Clarification Regarding Application of Appendix W Modeling Guidance for

the 1-hour NO2 National Ambient Air Quality Standard, March 01, 2011” recommends use of an in-

stack NO2/NOx ratio of 0.5, but allows different ratios to be used provided that available data justifies

use. Lower NO2/NOx ratios for boilers, blasting and compression ignition internal combustion engines

have been recommended by regulatory agencies including the Texas Commission on Environmental

Quality (TCEQ) and the San Joaquin Valley Air Pollution Control District (SJVAPCD). The value of

0.1 was the default value in the addendum to the AERMOD user guide “Addendum: User’s Guide for

the AMS/EPA Regulatory Model AERMOD, EPA-454/B-03-001, September 2004”. Because the

overwhelming majority of NOx emissions are from mobile sources, an in-stack ratio of 0.05 will be

used in this modeling. For analysis of the available data and justification pertaining to the NO2 to NOx

ratio of 0.05, see Appendix F.

The OLM method requires hourly background ozone values to calculate the conversion of NO2 to

NOx. Hourly background ozone values from the Chiricahua National Monument IMPROVE site will be

used (see previous section for explanation). This data base is complete with only 4% missing data.

The missing data will be replaced by the 1-hour average of the entire database. The OLMGROUP

option will also be used which essentially models all the plumes as one combined plume.

5.2 Receptor Network

Following the ADEQ Guidance, the receptor grid (see Figure 5.1) consisting of the following will be

modeled:

receptors spaced at 25 meters along the Process Area Boundary (PAB);

receptors spaced at 100 meters from the PAB to 1 kilometer;

receptors spaced at 500 meters from 1 kilometer to 5 kilometers;

receptors spaced at 1000 meters from 5 kilometers to 50 kilometers.


Based on the recommendation of the Forest Service, a second receptor grid consisting of receptors

at the Saguaro East National Park will also be modeled (see Figure 5.2). These receptors were

obtained from the Class I Area Receptor Database developed by the Forest Service.

5.3 Receptor Elevations

Receptor elevations will be determined from the National Elevation Dataset (NED) distributed by the

USGS, which are based on North American Datum 1927 (NAD27). This dataset has a resolution of

1/3 arc-second (or approximately 10 meters).

The NED data will be processed with AERMAP. AERMAP, like AERMET, is a preprocessor program

which was developed to process terrain data in conjunction with a layout of receptors and sources to

be used in AERMOD. For complex terrain situations, AERMOD captures the essential physics of

dispersion in complex terrain and therefore, needs elevation data that convey the features of the

surrounding terrain. In response to this need, AERMAP first determines the base elevation at each

receptor. AERMAP then searches for the terrain height and location that has the greatest influence

on dispersion for each individual receptor. This height is referred to as the hill height scale. Both the

base elevation and hill height scale data are produced by AERMAP as a file or files which are then

inserted into an AERMOD input control file.

5.4 Modeling Domain

The AERMAP terrain preprocessor requires the user to define a modeling domain. The modeling

domain is defined as the area that contains all the receptors and sources being modeled with a buffer

to accommodate any significant terrain elevations. Significant terrain elevations include all the terrain

that is at or above a 10% slope from each and every receptor. BEE-Line’s software automatically

calculates the modeling domain based on the receptor grid being used and identifies each 7.5-minute

DEM quadrangle that must be used in AERMAP to meet the 10% slope requirement.

5.5 Plume Depletion

One other option in the AERMOD model requires particle size data. This option is known as DDEP,

which specifies that dry deposition flux values will be calculated. If this option is selected, dry

removal (depletion) mechanisms (known as dry plume depletion (DRYDPLT) in the old ISC modeling

program and earlier versions of AERMOD) are automatically included in the calculated

concentrations. This option will be selected in the proposed modeling for receptors exhibiting high

particulate impacts in initial modeling runs.

5.6 Building Downwash

Building downwash effects will be evaluated by incorporating the appropriate building/structure

dimensions into the AERMOD input files using BEE-Line’s commercial version of EPA’s Building

Profile Input Program for PRIME (BPIPPRM) software. The BPIPPRM program is EPA approved and

includes the latest EPA building downwash algorithms. The downwash files generated by BPIPPRM

program will be provided in the final modeling report.


Figure 5.1 Receptor Grid Network Developed for the Rosemont Project Modeling Analysis


Figure 5.2 Receptor Network for Evaluating Impacts at the Saguaro East National Park


6. EMISSIONS MODELED AND SOURCE CHARACTERIZATION

A preliminary description of the planned equipment and emission generating processes at Rosemont

can be found in the previously referenced Mine Plan of Operations. A plan view map depicting the

facility layout by Year 5 is presented in Figure 6.1. A preliminary plan view of the ancillary operations,

to include locations of the primary crusher and flotation operations, is presented in Figure 6.2 .

6.1 Operational Years to Be Modeled

A preliminary summary of average and maximum mining rates and haul truck travel (vehicle miles) is

presented in Appendix D. This summary is subject to change depending upon any further

refinements to the mine plan. The mining information in Appendix D indicates:

The highest projected annual mining rate and highest haul truck travel outside the pit will

occur in Year 1 (approximately 115,000,000 tons of ore and waste per year; 2,237,113 haul

truck VMT).

The highest projected haul truck travel will occur in Year 5 (2,793,243 VMT per year).

Emissions from Rosemont will result from process equipment and mining operations. Process

equipment will be modeled at maximum capacity. Emissions from mining will depend upon the

mining rate and haul truck travel necessary to transport the ore and waste from the pit to the primary

crusher and the waste rock storage area.

Since haul truck travel will be the primary source of emissions (PM10 and tail pipe), Year 5 will be

modeled. Appendix D also shows that haul truck travel outside the pit will be a maximum during

Year 1. Since emissions outside the pit are expected to have a greater impact on ambient

concentrations than emissions in the pit, this year will also be modeled. Emissions, and therefore

impacts, during all other years will be less than during these two years.

6.1.1 Annual Criteria Pollutant Emissions Modeling

Annual impacts of particulate and gaseous emissions will be based upon emissions calculated using

the average daily process rates for Years 1 and 5. The average daily process rate is used for

determining annual impacts since it represents expected emissions over the course of a year.

6.1.2 Short-Term Criteria Pollutant Emissions Modeling

Short-term impacts (1-hour, 3-hour, 8-hour and 24-hour) will be based upon the emissions calculated

using the maximum daily process rates for Years 1 and 5. Short-term impacts are affected by peak

emission rates. These are better determined using the expected maximum daily process rates rather

than the average daily process rates. For Year 5, the maximum daily process rate is based on the

average daily process rate increased by a capacity factor of 20%.


Figure 6.1 Plan View Map of Operations Depicting Facility Layout by Year 5


Figure 6.2 Plan View Map of Operations Showing Updated Ancillary Operation Locations for Rosemont


6.2 Point Sources

Point sources at Rosemont will include dust collectors, hot water heaters, and emergency

generator(s). Emissions from these sources will be modeled as individual point sources. Dust

collectors or baghouses are anticipated to have ambient exit temperatures and will be modeled using

a stack temperature of 0°K per ADEQ guidance, thus forcing the model to use the ambient

temperature as the exit temperature. Stack parameters for the point sources will be based on design

parameters and/or conservative estimated values. Particulate emissions from the emergency

generators will not be included in the PM10 modeling since most other operations would be shut down

if the generators are needed. Therefore, the generators would not add to peak emissions being

modeled. Gaseous emissions from these sources, however, will be included in the gaseous modeling

runs. The point source emissions will be modeled using the particle size distribution shown in Table

A.13 of Appendix A.

6.3 Volume Sources

Due to the nature of Rosemont’s operations, a majority of sources will be modeled as volume

sources. They are described in the sections below.

6.3.1 Roads

A refined road network will be developed to depict the anticipated haul truck routes and dumping

locations during the year of the mine plan with the estimated greatest emissions, which will be the

basis of the emissions inventory that will be used for all of the modeling. Emissions due to haul road

and general plant traffic on the paved and unpaved road network will be modeled as volume sources

and the modeling parameters will be based on guidance from ADEQ and the AERMOD User’s Guide.

The modeling parameters will be set as follows:

volume height will be set equal to twice the height of the vehicles generating the emissions;

initial vertical dimension will be set equal to the volume height divided by 2.15;

release height will be set equal to half of the volume height; and

initial lateral dimension will be set to the width of the road divided by 2.15. The road will be

further divided into two lanes representing 2-way traffic (by request of the Forest Service)

The majority of emissions on the haul road network will be due to large haul trucks. The height of the

Haul Trucks obtained from the manufacturers data was 6.6 meters (21.6 feet). Thus, for each road

source the volume height will be set to 13 meters (twice the height of the vehicles generating the

emissions rounded to the nearest meter), the initial vertical dimension will be set to 6.05 meters

(volume height divided by 2.15), and the release height will be set to 6.5 meters (half of the volume

height). The road width was estimated to be 35 meters. Thus, the initial lateral dimension for each

volume will be set to 16.3 meters (width of 35 meters divided by 2.15).

The road sources will be placed along the road network at approximately 35 meter intervals.

According to the mine plan, during Year 1 of operations, 78% of the haul emissions would be


generated outside the pit whereas during Year 5, 58% would be generated outside the pit. In-pit

traffic versus out-of-pit traffic distributions will be taken into account when evaluating the location of

haul road emissions..

The emissions from dumping to the sulfide ore stockpile, waste rock stockpiles and to the leach pad

will also be modeled as volume sources. The height of the haul trucks obtained from the

manufacturers data was 6.6 meters (21.6 feet). Thus, for each source representing dumping, the

volume height will be set to 13 meters (twice the height of the vehicles generating the emissions

rounded to the nearest meter), the initial vertical dimension will be set to 6..05 meters (volume height

divided by 2.15), and the release height will be set to 6.5 meters (half of the volume height). The

width of the trucks (simulating the dump width) obtained from the manufacturers data was 8.7 meters

(28.5 feet). Thus the initial horizontal dimension will be set to 4 meters (volume width divided by

2.15). The haul road emissions will be modeled using the particle size distribution shown in Table A.4

of Appendix A.

6.3.2 Truck Unloading

Fugitive emissions from truck unloading at the primary crusher will be represented by a single volume

source. The side length will be set to 8.23 meters (approximate width of dump pocket) and therefore,

the initial horizontal dimension will be set to 1.91 meters (8.23/4.3). The vertical length will be set to 3

meters (vertical drop of dump pocket). Consequently, the initial vertical dimension will be set to 1.4

meters (3/2.15) and the release height will be set to 0 meters (dump pocket is at grade level).

6.3.3 Sulfide Ore Stockpile

Fugitive emissions due to wind erosion from the sulfide ore stockpile will be represented by a single

volume source. The side length obtained from the map was 318 meters (average width of the

stockpile) and therefore the initial horizontal dimension will be set to 74 meters (318/4.3). The vertical

will be set to 12 meters (average height of stockpile). Consequently, the initial vertical dimension will

be set to 5.6 meters (12/2.15) and the release height will be set to 6 meters (half of the volume height

of 12 meters).

6.3.4 Tailings Stockpile

Fugitive emissions due to wind erosion from the Tailings stockpile will be represented either by a

single volume source or by multiple volume sources. If the stockpile is modeled as a single volume

source, then based on the area of tailings stockpile obtained from the map, the side length was

estimated to be 2464 meters; and therefore the initial horizontal dimension will be set to 573 meters

(2464/4.3). The vertical will be set to 12 meters (average height of stockpile). Consequently, the

initial vertical dimension will be set to 5.6 meters (12/2.15) and the release height will be set to 6

meters (half of the volume height of 12 meters). A similar approach will be followed if the Tailing

stockpile is modeled as multiple volume sources.

6.3.5 Conveyor Transfer Points

Fugitive emissions from conveyor transfer points will be represented by single volume sources. The

side length will be set to 2 meters (approximate width of the conveyors) and therefore, the initial


horizontal dimension will be set to 0.5 meters (2/4.3). The vertical length will be set to 3 meters

(approximate height of material drops from the conveyors). Consequently, the initial vertical

dimension will be set to 0.7 meters (3/4.3). The release height will be set to 3 meters (assumed

height of conveyors, except for the conveyors feeding the coarse ore stockpile. The release heights

for these sources will be set to the actual height of the conveyor at the top of the stockpile. Transfer

emissions will be modeled using the particle size distribution shown in Table A.7 of Appendix A.

6.3.6 Gaseous Emissions Due to Blasting

The gaseous emissions due to blasting in the pit will be modeled as volume sources. The fugitive

gaseous emissions due to blasting in the pit were equally spaced at 250 meter intervals (arbitrarily

selected) over the pit area. The side length of each volume source was set at 61.0 meters

(represents the average width of a blast) and therefore, the initial horizontal dimension was set to

14.2 meters (61.0/4.3).

A typical blast can send emissions 30 meters into the air. Consequently, a conservative vertical

dimension of 20 meters was assigned to the volume sources representing the blasting emissions.

Thus the initial vertical dimension of each source will be set to 9.3 meters (20/2.15) and the release

height will be set to 10 meters (1/2 of the vertical dimension of 20 meters). The base elevation for the

volume sources in the pit will be set to the average elevation between the lowest and highest

elevation of the terrain defining the bottom and top of the pit, based on the assumption that these

emissions must rise above the walls of the pit before being dispersed downwind. Since Rosemont

anticipates blasting to occur only between 12 PM and 4 PM, the variable emission rate option

HROFDY in AERMOD will be used to model the emissions between the above 4 hour interval every

day. The PM10 emissions from blasting will also be modeled as volume sources and will use the

particle size distribution shown in Table A.10. For evaluating the 1-hr averaged impacts from NO2,

SO2 and CO, blasting emissions will be set to occur every hour between 12 PM to 4 PM. Test

modeling runs indicated that the maximum impact due to blasting emissions occurred at 4 PM every

day. Therefore for all impact evaluations greater than the 1-hr averaged impacts, blasting will be set

to occur at 4 PM every day. The HROFDY variable emissions rate option in AERMOD will be used

for this.

6.3.7 Open Pit

Fugitive particulate emissions from the open pit at Rosemont will be modeled using the open pit

source model as defined by the AERMOD model (only particulate emissions are considered with the

open pit source model). The open pit source parameters, easterly length, northerly length and

volume, will be based on the length and width dimensions of the rectangle drawn to simulate the pit

shape in the model and the anticipated depth of the pit in the worst case year. The release height will

be set to zero. The Year 5 mine plan shows a berm developed on the east and south side of the

process area boundary. This 150 foot berm essentially covers the waste dump and leach pads on the

east and south. Therefore the emissions generated at the leach pad and waste dump will be modeled

as a second pit with a depth of 150 feet.

The open pit source option in the AERMOD model requires particle size distribution data in the form

of the mass-mean particle diameter, mass weighted size distribution, and particle density. Table A.4


shows the particle size distribution developed for haul road emissions. This distribution will be used

for the open pit source since a majority of the emissions in the pit are haul road emissions.

A particle density of 2.44 gm/cm3, the other required input variable, is initially proposed for use in the

modeling as a representative value of the average density of the various rock materials (overburden,

waste rock, ore) that will be mined. A more specific value will be used if specific density data for the

materials to be mined at Rosemont become available.

6.3.8 Tail Pipe Emissions

Tail pipe emissions from mobile sources will be distributed among road emission sources and the

open pit source. The amount of emissions assigned to each individual road segment and to the pit

will be based on an evaluation of the vehicle miles travelled (VMT) along each road segment and

inside the pit. Appendix D provides a breakdown of the in-pit versus out-of-pit traffic. All tailpipe

particulate emissions will be modeled as PM2.5 as recommended by ADEQ. See supporting email

correspondence with ADEQ in Appendix E.


7. ALTERNATIVE SCENARIOS

Rosemont is currently developing an alternative scenario called the Barrel Only Alternative. The

modeling for the proposed alternative scenario will follow the same procedures as outlined in the

previous sections of this document. The Barrel Only Alternative re-routes some of the haul roads and

dumping locations, thus the source locations would differ from those in the Proposed Action.

However, the modeling methodology for the Barrel Only Alternative will follow the methodology for the

Proposed Action.


8. EVALUATION OF DISPERSION MODELING RESULTS

Evaluation of protection of the NAAQS will be performed by comparing the maximum modeled

impacts to the applicable standards. The final impact analysis will include all information necessary

for this evaluation including: (a) background concentrations; (b) source location map; (c) complete list

of source parameters; (d) complete modeling input and output files; and (e) graphic presentations of

the modeling results for each pollutant, showing the magnitude and location of the maximum ambient

impacts. The methodology for evaluating protection of the NAAQS for each pollutant of interest is

described below.

8.1 CO Evaluation

The modeled highest 2nd

high 1-hour and 8-hour CO concentrations will be determined. These

predicted concentrations will be added to the 1-hour and 8-hour CO background concentrations to

determine the maximum ambient CO concentrations for both Year 1 and Year 5. The results will be

compared to the applicable 1-hour and 8-hour CO NAAQS of 40,000 g/m3 and 10,000 g/m

3

respectively.

8.2 NO2 Evaluation

Although emissions are estimated in terms of total NOx, only NO2 has a NAAQS. NOx emissions from

fuel combustion sources are primarily NO (nitrous oxide) which gradually converts to NO2 over time.

Comparison of the maximum predicted NOx concentrations with the annual NAAQS for NO2 thus

represents a very conservative method of demonstrating protection of NAAQS. Modeling for the 1-

hour NO2 concentration will be conducted using an in-stock NO2/NOx ratio as described in Section

5.1.

The modeled highest annual NOx concentration will be added to the annual NO2 background

concentration, and the 98th percentile 1-hour NOx concentration will be added to the 1-hour

background NO2 concentration for Years 1 and 5. The results will be compared to the applicable 1-hr

and annual NO2 NAAQS of 188.6 g/m3 and 100 g/m

3, respectively.

8.3 PM10 Evaluation

The modeled highest 4th high 24-hour PM10 concentration for Year 1 and Year 5 will be added to the

24-hour PM10 background concentration. The highest modeled annual PM10 concentrations for Years

1 and 5 will be added to the annual PM10 background concentration. These concentrations will be

compared to the applicable 24-hour and annual PM10 NAAQS of 150 g/m3 and 50 g/m

3,

respectively, to evaluate protection of the PM10 NAAQS.

8.4 SO2 Evaluation

The 99th percentile of the 1-hour daily maximum SO2 concentration and the highest 2

nd high 3-hour

SO2 concentration will be added to their respective background concentrations. The resulting

predicted concentrations for Years 1 and 5 will be compared to the 1-hour and 3-hour SO2 NAAQS of

196 g/m3 and 1,300 g/m

3, respectively. The 24-hour and annual SO2 concentrations will be


evaluated for both Years 1 and 5 and compared to the applicable 24-hour and annual SO2 NAAQS of

365 g/m3 and 80 g/m

3, respectively.

8.5 PM2.5 Evaluation

The three year average of the 98th percentile 24-hour concentration and highest annual concentration

for Year 1 and Year 5 modeling will be added to the 24-hour and annual PM2.5 background

concentrations. These concentrations will be compared to the applicable 24-hour and annual PM2.5

NAAQS of 35 g/m3 and 15 g/m

3, respectively.

APPENDIX A

PARTICLE SIZE DISTRIBUTIONS

Rosemont AERMOD Modeling Protocol A-1

A. PARTICLE SIZE DISTRIBUTIONS

The Dry Deposition option in AERMOD calculates the fraction of the particulate emissions in the

plume that are removed by interaction with the ground surface or vegetation, thus providing a better

estimate of the concentration of pollutants downwind from the source. The use of this option in

AERMOD requires particle size distribution data in the form of the mass-mean particle diameter,

mass weighted particle size distribution, and particle density. EPA modeling guidance does not

specify any default values and this type of data is not readily available. The table below shows the

particle size categories and the corresponding mass-mean particle diameters that will be used for

modeling for the Rosemont project. The following sections describe the methodologies used to

estimate the mass-mean particle diameter, particle density and mass weighted particle size

distributions for various emission sources based on AP 42 emission factors.

A.1 Mass-Mean Particle Diameters

The expected mass mean particle diameter for particle size ranges between 0 and 10 microns in

diameter was calculated using the formula below.

where: d = mass-mean particle diameter

d1 = low end of particle size category range

d2 = high end of particle size category range

Table A.1 Mass-Mean Particle Diameters

Particle Size Category Range

(microns)

Mass-Mean Particle Diameter (microns)

0 - 3.5 2.20

3.5 - 5 4.29

5 - 7 6.05

7 - 8.5 7.77

8.5 - 10 9.27

A.2 Particle Density

A particle density of 2.44 gm/cm3 will be used for modeling. This value has previously been approved

for use in similar modeling analyses by ADEQ, and is based on a weighted average of the densities

of various rock materials (overburden, waste rock, ore) at copper mines.

A.3 Haul Road Sources - Particle Size Distribution

Section 13.2.2 of EPA's AP-42, Compilation of Air Pollutant Emission Factors, provides in

Equation 1a a method to calculate emission factors for unpaved industrial roads. Based on a mean


haul truck weight of 305 tons and a silt content of 5% and using Equation 1a, the estimated emission

factors for haul trucks at Rosemont were calculated. The emission factors were used to determine

the distribution of emissions for particles with nominal diameters less than 30, 10 and 2.5 m by

calculating the percentage of PM30 emissions that can be attributed to PM10 and PM2.5 emissions.

The emission factors and distribution of PM30 emissions are presented in Table A.2.

Equation 1a

where k, a, b, c and d are empirical constants and:

E = size-specific emission factor (lb/VMT)

s = surface material silt content (%)

W = mean vehicle weight (tons)

Table A.2 Haul Road Emission Factors and Distribution of PM30 Emissions

Particle Size Diameter (microns)

Emission Factor

(lb/VMT)a

Percentage of PM30 Emissions

(%)

30 21.25 100.00

10 5.46 25.69

2.5 0.55 2.59

a Based on Equation 1a, AP-42 Section 13.2.2, and AP-42 Table 13.2.2-2

The percentage of PM30 emissions was plotted against the particle size diameter for each of the given

particle sizes. A 2nd

degree polynomial equation was used to fit the particle size diameter and

percentage data as shown in Figure A.1.

Figure A.1 Particle Size Distributions for Haul Roads Based on AP-42 Emission Factors

y = 0.0231x2 + 2.7924x - 4.5369 R² = 1

0

20

40

60

80

100

120

0 10 20 30 40

%

Particle Size (µm)

Particle Size Distribution for Haul Roads Based on AP-42 Emission Factors

Particle Size Distribution

Poly. (Particle Size Distribution)


The percentage of particulates in each mass-mean particle diameter category described in Table A.1

was calculated based on the polynomial equation shown in Figure A.1. These percentages and the

cumulative distribution of emissions between 0-10 microns was determined as shown in Table A.3.

Table A.3 Haul Road Cumulative Particle Size Distribution



(%)

Cumulative Distribution of Particle Sizes

(%)

2.20 1.72 7.36

4.29 7.87 33.72

6.05 13.20 56.58

7.77 18.55 79.52

9.27 23.33 100.00

The particle size distribution for each particle size category range was then determined based on the

0 to 10 micron portion of the PM30 distribution. These particle size distribution percentages as shown

in Table A.4 will be used in the modeling.

Table A.4 Particle Size Distribution - Haul Roads


(microns)



(%)

0 - 3.5 2.20 7.36

3.5 - 5 4.29 26.35

5 - 7 6.05 22.86

7 - 8.5 7.77 22.94

8.5 - 10 9.27 20.48

A.4 Aggregate Handling - Particle Size Distribution

Section 13.2.4 of AP-42 lists equations to estimate emission factors for aggregate handling

processes. The emission factors for different particle sizes are determined by the particle size

multipliers that are given in Section 12.2.4.3 of AP-42. These particle size multipliers were used to

determine the distribution of emissions for particles with nominal diameters less than 30, 10 and

2.5 m by calculating the percentage of PM30 emissions that can be attributed to PM10 and PM2.5

emissions. The aggregate handling particle size multipliers and the distribution of PM30 emissions are

presented in Table A.5.


Table A.5 Aggregate Handling Particle Size Multipliers and Distribution of PM30 Emissions


Particle Size Multiplier


(%)

30 0.74 100.00

15 0.48 64.86

10 0.35 47.30

5 0.20 27.03

2.5 0.053 7.16



degree polynomial equation was used to fit the emission factor and percentage

data as shown in Figure A.2.

Figure A.2 Particle Size Distribution for Aggregate Handling Based on AP-42 Particle Size Multipliers




y = -0.0833x2 + 5.9717x - 4.4505 R² = 0.9953

0

20

40

60

80

100

120

0 10 20 30 40

%

Particle Size (µm)

Particle Size Distribution for Aggregate Handling Based on AP-42 Particle Size Multipliers




Table A.6 Aggregate Handling Cumulative Particle Size Distribution



(%)


(%)

2.20 8.28 18.94

4.29 19.64 44.88

6.05 28.63 65.44

7.77 36.92 84.39

9.27 43.75 100.00




Table A.7 Particle Size Distribution – Aggregate Handling


(microns)



(%)

0 - 3.5 2.20 18.94

3.5 - 5 4.29 25.95

5 - 7 6.05 20.56

7 - 8.5 7.77 18.95

8.5 - 10 9.27 15.61

A.5 Blasting - Particle Size Distribution

AP-42 Table 11.9-1 lists a predictive equation for estimating the PM30 emission factor for blasting

based on the western surface coal mining process. This table also lists scaling factors used to

multiply with the predictive equation to estimate emission factors for other particle sizes. The scaling

factors for blasting were used to determine the distribution of emissions for particles with nominal

diameters less than 30, 10 and 2.5 m by calculating the percentage of PM30 emissions that can be

attributed to PM10 and PM2.5 emissions. The scaling factors and distribution of PM30 emissions are

presented in Table A.8.

Table A.8 Blasting Scaling Factors and Distribution of PM30 Emissions


Scaling Factor Percentage of PM30

Emissions (%)

30 1 100

10 0.52 52

2.5 0.03 3 aPredictive equation = 0.000014(A)^(1.5) ; A = Area of blast






Figure A.3 Particle Size Distribution for Blasting Based on AP-42 Scaling Factors




Table A.9 Blasting Cumulative Particle Size Distribution



(%)


(%)

2.20 0.69 1.43

4.29 16.23 33.83

6.05 28.30 58.99

7.77 39.20 81.71

9.27 47.97 100.00




y = -0.1503x2 + 8.4121x - 17.091 R² = 1

0

20

40

60

80

100

120

0 10 20 30 40

%

Particle Size (µm)

Particle Size Distribution for Blasting Based on AP-42 Scaling Factors




Table A.10 Particle Size Distribution - Blasting


(microns)



(%)

0 - 3.5 2.20 1.43

3.5 - 5 4.29 32.40

5 - 7 6.05 25.16

7 - 8.5 7.77 22.71

8.5 - 10 9.27 18.29

A.6 Point Sources - Particle Size Distribution

Page B.2-6, Appendix B.2 of AP-42 lists the collection efficiency of fabric filters used in baghouses for

various particle sizes. These collection efficiencies were used along with emission factors for

aggregate handling processes (Section 13.2.4 of AP-42) to calculate particle size distributions. The

aggregate handling process emission factors for various particle sizes depend upon the particle size

multiplier. These particle size multipliers were used to determine the distribution of emissions for

particles with nominal diameters less than 30, 15, 10, 5 and 2.5 m by calculating the percentage of

PM30 emissions that can be attributed to PM10 and PM2.5 emissions. The scaling factors and

distribution of PM30 emissions are presented in Table A.11.

Table A.11 Point Source Emission Factors and Distribution of PM30 Emissions


Emission Factor (lb/VMT)

a


(%)

30 0.74 100.00

15 0.48 64.86

10 0.35 47.30

5 0.20 27.03

2.5 0.053 7.16






Figure A.4 Particle Size Distribution for Point Source Emissions Based on AP-42 Emission Factors




Table A.12 Point Source Cumulative Particle Size Distribution

Mass-Mean Particle

Diameter (microns)


(%)


(%)

2.20 8.28 18.94

4.29 19.64 44.88

6.05 28.63 65.44

7.77 36.92 84.39

9.27 43.75 100.00


0 to 10 micron portion of the PM30 distribution and adjusted based on fabric filter collection

efficiencies of 99% for the 2.5 micron fraction and 99.5% for the remaining size fractions. These

particle size distribution percentages as shown in Table A.13 will be used in the modeling.

y = -0.0833x2 + 5.9717x - 4.4505 R² = 0.9953

0

20

40

60

80

100

120

0 10 20 30 40

%

Particle Size (µm)

Particle Size Distribution for Point Source Emissions Based on AP-42 Emission Factors




Table A.13 Particle Size Distribution - Point Sources


(microns)



(%)

0 - 3.5a 2.20 31.84

3.5 - 5b

4.29 21.81

5 - 7b

6.05 17.29

7 - 8.5b

7.77 15.93

8.5 - 10b

9.27 13.12 a 99% collection efficiency for fabric filter dust collectors used.

b 99.5% collection efficiency for fabric filter dust collectors used.

A.7 Paved Road Sources - Particle Size Distribution

Section 13.2.1 of AP 42 lists equations to estimate emission factors for Paved Roads. The emission

factors for different particle sizes are determined by the particle size multipliers that are given in

Section 13.2.1 of AP42. These particle size multipliers were used to determine the distribution of

emissions for particles with nominal diameters less than 30, 15, 10, 5 and 2.5 m by calculating the

percentage of PM30 emissions that can be attributed to PM10 and PM2.5 emissions. The paved road

particle size multipliers and the distribution of PM30 emissions are presented in Table A.14.

Table A.14 Paved Road Emission Factors


Particle Size Multiplier

a

Percentage Distribution (%)

30 0.011 100.00

15 0.0027 24.55

10 0.0022 20.00

2.5 0.00054 4.91

a AP42 Section 13.2.1 Table 13.2.1-1. Used in equation 1 of AP42 Section 13.2.1.3

The percentage of PM30 emissions was plotted against the particle size diameters for each of the

given particle sizes. A 2nd

degree polynomial equation was used to fit the particle size diameter and

percentage data as shown in Figure A.5..


Figure A.5 Particle Size Distribution - Paved Road Source Emissions based on AP 42 Emission Factors




Table A.15 Paved Roads Cumulative Particle Size Distribution



(%)


(%)

2.20 6.15 44.09

4.29 7.32 52.49

6.05 9.04 64.85

7.77 11.38 81.61

9.27 13.94 100.00

The particle size distribution for each particle size category range was determined based on the 0 to

10 microns portion of the PM30 distribution. These particle size distribution percentages as shown in

Table A.16 will be used in the modeling.

y = 0.1089x2 - 0.1468x + 5.9426 R² = 0.9932

0.00

20.00

40.00

60.00

80.00

100.00

120.00

0 10 20 30 40

%

Particle Size (µm)

Particle Size Distribution for Paved Roads based on AP 42 Emission Factors

Paved Road Particle Size Distribution

Poly. (Paved Road Particle Size Distribution)


Table A.16 Particle Size Distribution - Paved Road Sources


(microns)



(%)

0 - 3.5a 2.20 44.09

3.5 - 5b 4.29 8.40

5 - 7b 6.05 12.36

7 - 8.5b 7.77 16.76

8.5 - 10b 9.27 18.39

APPENDIX B

QUARTERLY PM10 MONITORING SUMMARIES

Rosemont AERMOD Modeling Protocol B-1

Table B.1 Summary of 24-Hour PM10 Concentrations (g/m3)

July 2006-June 2007

Time Period

Valid Samples

Arithmetic Mean

Highest 2nd Highest 3rd Highest

3rd Quarter

06 13 24.6 71.3 27.0 26.8

4th Quarter

06 14 8.3 18.7 17.7 10.6

1st Quarter

07 15 2.3 7.0 5.5 4.6

2nd Quarter

07 15 17.6 28.7 27.0 25.6

Average 14.25 13.2 N/A N/A N/A

Highest Overall

N/A N/A 71.3 27.0 26.8


July 2007-June 2008

Time Period

Valid Samples

Arithmetic Mean


3rd Quarter

07

13 19.2 40.3 21.7 20.8

4th Quarter

07

15 5.3 11.9 11.9 8.0

1st Quarter

08

16 4.1 13.5 9.6 7.7

2nd Quarter

08

15 19.5 32.6 28.2 25.2


Highest Overall

N/A N/A 40.3 28.2 25.2

Rosemont AERMOD Modeling Protocol B-2


July 2008-June 2009

Time Period

Valid Samples

Arithmetic Mean


3rd Quarter

08

14 15.3 24.5 21.2 20.0

4th Quarter

08

15 8.5 31.6 15.1 12.7

1st Quarter

09

15 8.0 17.9 17.8 17.6

2nd Quarter

09

16 10.0 15.4 12.9 12.9

Average 15 10.45 N/A N/A N/A

Highest Overall

N/A N/A 31.6 21.2 20.0

Table B.4 Summary of Annual PM10 Concentrations (g/m3)

Time Period Valid

Samples Arithmetic

Mean Highest 2nd Highest 3rd Highest

July 2006-June 2007 14.25 13.2 71.3 27.0 26.8

July 2007- June 2008 14.8 12.0 40.3 28.2 25.2

July 2008- June 2009 15 10.45 31.6 21.2 20.0


Highest Overall N/A N/A 71.3 28.2 26.8

APPENDIX C

STATISTICAL EVALUATION OF AMBIENT

PM10 MEASUREMENTS

Rosemont AERMOD Modeling Protocol C-1

C.1 INTRODUCTION AND BACKGROUND INFORMATION

Ambient monitoring at the Rosemont site for PM10 concentrations was performed and recorded from

June 16, 2006 to June 30, 2009. The PM10 concentration measurements, as a function of the date they

were recorded (time series plot) are presented in Figure C.1. Within this data, there are two

concentration measurements that appear to be outlying data. These data points are indicated in red in

Figure C.1.

Figure C.1 PM10 Concentration Measurements

The remainder of this appendix will statistically analyze these two data points to determine:

If each data point can statistically be labeled as an outlier; and

What the probability is of a future occurrence greater than or equal to each data point.

The results of the above analyses will determine if the data points can statistically be eliminated during

further PM10 concentration data analysis. All graphs and statistical data presented in this appendix have

been generated using Minitab. The Minitab output is presented in Appendix C1.

C.2 OUTLIER ANALYSIS

C.2.1 ANALYSIS

An outlier is defined as a data point:

1. Greater than 1.5 interquartile ranges but less than 3 interquartile ranges from the third quartile

value; or

06/01/0902/01/0910/01/0806/01/0802/01/0810/01/0706/01/0702/01/0710/01/0606/01/06

80

70

60

50

40

30

20

10

0

Date of Measurement

PM

10

Co

nce

ntr

ati

on

s (

µg

/m

3)


2. Less than 1.5 interquartile ranges but more than 3 interquartile ranges from the first quartile

value.

Additionally, an extreme outlier is defined as a data point:

A. Greater than 3 interquartile ranges from the third quartile value; or

B. Less than 3 interquartile ranges from the first quartile value.

These are widely accepted definitions and can be found in any statistics textbook. For reference, the

definitions can be located on page 33 of the Third Edition of Engineering Statistics (Montgomery, Runger,

Hubele).

As shown in Figure C.1, the data points being analyzed are greater than the remaining PM10

concentration measurements. Therefore, only Definition 1 for an outlier and Definition A for extreme

outlier will be considered further in this analysis.

The statistical data for the PM10 concentration measurements are presented and defined in Table C.1.

This information is also graphically shown in the box plot presented in Figure C.2.

Figure C.2 Box Plot for PM10 Concentration Measurements

As shown in Table C.1 and Figure C.2, the 40.3 µg/m3 data point meets the definition of an outlier while

the 71.3 µg/m3 data point meets the definition of extreme outlier. The 71.3 µg/m

3 data point is within four

interquartile ranges from the third quartile value.

80706050403020100

PM10 Concentrations (µg/m3)

9.8

40.3 71.3

First

Quartile Third

Quartile Median

(Second

Quartile)

Lower

Whisker

Upper

Whisker

Outlier Extreme

Outlier

IQR 1.5 IQR

Data

Points

1.5 IQR 1.5 IQR


Table C.1 Statistical Data for the PM10 Concentration Measurements

Statistical Data Definition Value for the PM10

Concentration Measurements

Minimum Value The smallest observation in the data set. 0.30 µg/m3

First Quartile Value The value that has 25% of the data points less than it and 75% of

the data points greater than it. 4.65 µg/m

3

Median (Second Quartile) Value The value that has 50% of the data points less than it and 50% of


3

Mean The location or central tendency of the data points. 11.625 µg/m3

Third Quartile Value The value that has 75% of the data points less than it and 25% of


3

Maximum Value The largest observation in the data set. 71.30 µg/m3

Interquartile Range (IQR) The difference between the third and first quartile values. 13.40 µg/m3

Lower Whisker Endpoint The smallest data point within 1.5 interquartile ranges from the first

quartile value. 0.30 µg/m

3

Upper Whisker Endpoint The largest data point within 1.5 interquartile ranges from the third

quartile value. 32.60 µg/m

3

3rd Quartile Value + 1.5 IQR The third quartile value plus 1.5 times the interquartile range. 38.15 µg/m3

3rd Quartile Value + 3 IQR The third quartile value plus 3 times the interquartile range. 58.25 µg/m3

3rd Quartile Value + 4 IQR The third quartile value plus 4 times the interquartile range. 71.65 µg/m3


C.2.2 CONCLUSION BASED ON OUTLIER ANALYSIS

The high value of the extreme outlier (71.3 µg/m3) compared to the other PM10 concentration

measurements recorded during the monitoring program (median value of 9.80 µg/m3) indicates that a

highly unusual event or some error occurred during the measurement or processing of the data (e.g.

recording of an incorrect filter weight). Consequently, it is unrealistic to include this data point in further

PM10 concentration data analysis.

Instead, the 71.3 µg/m3 data point will be replaced by a value equivalent to the outlier data point (40.3

µg/m3). Although the 40.3 µg/m3 data point is a statistical outlier, it is within 2.15 µg/m

3 of being

considered a non-outlier data point. Therefore, retaining the 40.3 µg/m3 data point in future PM10

concentration data analysis and replacing the 71.3 µg/m3 data point with 40.3 µg/m

3 is a conservative

method used to approximate realistic high concentration measurements for the Rosemont project.

C.3 PROBABILITY ANALYSIS

C.3.1 DETERMINATION OF DISTRIBUTION

In order to determine the probability of occurrence of future PM10 concentration measurements, the

statistical distribution of the data set needs to be determined. Probability plots are commonly used to

evaluate the fit of a statistical distribution to a data set. They use a scale specific to a certain type of

statistical distribution and plot the ordered data points against the percentage of data points in the data

set that are less than or equal to the data points. If the plotted points approximately form a straight line,

the data set can be assumed to follow the specified distribution.

Probability plots for the PM10 concentration measurements were made in Minitab for 14 different types of

statistical distributions. These probability plots are presented with the Minitab output in Appendix C1. As

shown in Appendix C1, two probability plots were made for each type of distribution including:

1) All PM10 concentration measurement data points; and

2) All PM10 concentration measurement data points excluding 71.3 µg/m3, which was replaced by

40.3 µg/m3 as suggested in Section C.2.2.

For each probability plot, Minitab records the Anderson-Darling (AD) statistic, which is used to measure

how well the statistical distribution fits the data set. For a given data set, the better the statistical

distribution fits the data, the smaller the AD statistic will be.

The AD statistics for each probability plot associated with the PM10 concentration measurements are

presented in Table C.2. As shown in Table C.2, the Weibull distribution has the lowest AD statistic when

including all PM10 concentration measurements (from now on referred to as the CM data set). Although

the AD statistic increases slightly when replacing the 71.3 µg/m3 data point with 40.3 µg/m

3 (from now on

referred to as CM* data set), the Weibull distribution still results in the lowest AD statistic. Therefore, it is

assumed that the CM and CM* data sets have a Weibull distribution. The following probability analysis

will be based on a Weibull distribution.


Table C.2 Anderson-Darling Statistic for Statistical Distributions of the PM10 Concentration Measurements

Type of Distribution

Anderson-Darling Statistic

CM - All PM10 Concentration Measurements (μg/m3)

CM* - All PM10 Concentration Measurements (μg/m3) Except

with 71.3 Replaced by 40.3

Normal 3.285 2.989

Lognormal 3.744 3.870

3-Parameter Lognormal 1.471 1.524

Exponential 3.280 3.586

2-Parameter Exponential 2.727 3.014

Smallest Extreme Value 16.867 6.735

Weibull 0.796 0.955

3-Parameter Weibull 1.012 1.066

Largest Extreme Value 1.643 1.761

Logistic 2.809 2.906

Loglogistic 2.742 2.832

3-Parameter Loglogistic 1.824 1.864

Gamma 1.082 1.224

3-Parameter Gamma 1.144 1.261

C.3.2 PROBABILITY ANALYSIS

Since the CM and CM* data sets have been determined to have a Weibull distribution, the following

Weibull probability density and cumulative distribution functions can be utilized:

x- exp

x xf

1

, for x > 0, β > 0, δ > 0 Weibull Probability Density Function

x-exp - 1 F(x) Weibull Cumulative Distribution Function

where:

x = Weibull random variable;

β = shape parameter

δ = scale parameter


The probability density function, f(x), produces the probability of a data point, x, occurring in a future

sample. The cumulative distribution function, F(x), produces the probability of a future sample being

equal to or less than the specified data point, x. The probability of a future sample being greater than a

specified data point, x, can be determined by subtracting the value found using the cumulative distribution

function from 100% probability (1-F(x)). Furthermore, the number of samples expected to occur before

obtaining a sample greater than a specified data point, x, can be determined by taking the multiplicative

inverse (reciprocal) of 1-F(x).

The output of the Weibull probability density function for the CM and CM* data sets are presented in

Figure C.3. The output of the Weibull cumulative distribution function for the CM and CM* data sets are

presented in Figure C.4. The CM data set has a shape parameter of 1.258 and a scale parameter of

12.48 while the CM* data set has a shape parameter of 1.311 and a scale parameter of 12.38. The

shape and scale parameters were determined by Minitab and are shown in the Minitab output in

Appendix C1.

The specific numerical output of the probability density function and the cumulative distribution function

for PM10 concentrations 40.3 µg/m3 and 71.3 µg/m

3 are presented in Table C.3. The probabilities of a

future sample being greater than 40.3 µg/m3 and 71.3 µg/m

3 are also presented in Table C.3.

Additionally, Table C.3 presents the number of samples expected to occur before obtaining a sample

greater than 40.3 µg/m3 and 71.3 µg/m

3.

Figure C.3 Weibull Probability Density for PM10 Concentration Measurements

806040200

0.06

0.05

0.04

0.03

0.02

0.01

0.00

806040200

CM


Pro

ba

bili

ty D

en

sit

y F

un

cti

on

CM*


Figure C.4 Weibull Cumulative Distribution for PM10 Concentration Measurements

Table C.3 Probability Data for the PM10 Concentration Measurements

Probability/Expected Value CM Data Set CM* Data Set

40.3 µg/m3 71.3 µg/m

3 40.3 µg/m

3 71.3 µg/m

3

Probability of a Data Point Occurring in a

Future Sample (Weibull Probability Density

Function, f(x))

0.002 0.00002 0.001 0.000009

Probability of a Future Sample Being Equal to

or Less than the Data Point (Weibull

Cumulative Distribution Function, F(x))

0.987 0.9999 0.991 0.99995

Probability of a Future Sample Being Greater

than the Data Point (1-F(x)) 0.013 0.0001 0.009 0.00005

Number of Samples Expected to Occur

Before Obtaining a Sample Greater than the

Data Point (1/(1-F(x)))

79 7,760 110 20,488

806040200

1.0

0.8

0.6

0.4

0.2

0.0

806040200

CM


Cu

mu

lati

ve

Dis

trib

uti

on

Fu

ncti

on

CM*


C.3.3 CONCLUSION BASED ON PROBABILITY ANALYSIS

As shown in Table C.3, the probability of a future PM10 concentration measurement being greater than

71.3 µg/m3 is extremely low, regardless of if the 71.3 µg/m

3 data point is included or replaced by 40.3

µg/m3 in the data set being analyzed (0.01% and 0.005% probability, respectively). Furthermore, for a

sampling plan that obtains a PM10 concentration measurement once every six days (identical to the

sampling plan used to obtain the data analyzed in this appendix), it would be expected to see a PM10

concentration measurement greater than 71.3 µg/m3 approximately once every 127 or 336 years, using

the CM or CM* data sets, respectively. Therefore, combining the extreme outlier determination with the

low probability of reoccurrence, it is determined to be unrealistic to use the 71.3 µg/m3 data point in future

PM10 concentration data analysis.

Since the probability of a future PM10 concentration measurement being greater than the 40.3 µg/m3 is

approximately 1% and this future measurement is expected to occur during the life of the RCP (using a

once every six day sampling plan), the 40.3 µg/m3 data point should be included in future PM10

concentration data analysis.


APPENDIX C.1

MINITAB OUTPUT

Rosemont AERMOD Modeling Protocol C.1-1

Data Display

PM10

Row Date Concentrations

1 06/16/06 15.0

2 07/16/06 71.3

3 07/22/06 20.6

4 07/28/06 11.5

5 08/03/06 10.8

6 08/09/06 21.0

7 08/15/06 27.0

8 08/21/06 26.8

9 08/27/06 25.2

10 09/02/06 18.6

11 09/08/06 20.9

12 09/14/06 22.4

13 09/20/06 20.7

14 09/26/06 22.4

15 10/02/06 17.7

16 10/08/06 2.7

17 10/20/06 6.0

18 10/26/06 10.6

19 11/07/06 7.0

20 11/13/06 9.5

21 11/19/06 8.7

22 11/25/06 8.2

23 12/01/06 4.7

24 12/07/06 18.7

25 12/13/06 8.3

26 12/19/06 4.6

27 12/25/06 2.8

28 12/31/06 6.3

29 01/06/07 4.4

30 01/12/07 2.0

31 01/18/07 0.5

32 01/24/07 0.3

33 01/30/07 0.3

34 02/05/07 1.5

35 02/11/07 0.9

36 02/17/07 1.1

37 02/23/07 1.0

38 03/01/07 5.5

39 03/07/07 4.6

40 03/13/07 1.4

41 03/19/07 1.5

42 03/25/07 2.5

43 03/31/07 7.0

44 04/06/07 10.2

45 04/12/07 27.0

46 04/18/07 25.6


47 04/24/07 4.4

48 04/30/07 21.6

49 05/06/07 3.8

50 05/12/07 10.9

51 05/18/07 21.3

52 05/24/07 16.4

53 05/30/07 16.9

54 06/05/07 28.7

55 06/11/07 9.0

56 06/17/07 24.3

57 06/23/07 22.6

58 06/29/07 21.5

59 07/05/07 40.3

60 07/23/07 11.6

61 07/29/07 18.3

62 08/04/07 20.8

63 08/10/07 17.7

64 08/16/07 19.2

65 08/22/07 19.2

66 08/28/07 19.7

67 09/03/07 14.3

68 09/09/07 15.6

69 09/15/07 21.7

70 09/21/07 14.2

71 09/27/07 17.1

72 10/03/07 8.0

73 10/09/07 7.4

74 10/15/07 2.4

75 10/21/07 11.9

76 10/27/07 11.9

77 11/02/07 6.2

78 11/08/07 6.9

79 11/14/07 4.5

80 11/20/07 4.6

81 11/26/07 5.4

82 12/02/07 2.7

83 12/08/07 1.7

84 12/14/07 1.7

85 12/20/07 3.5

86 12/26/07 1.4

87 01/01/08 5.6

88 01/07/08 0.7

89 01/13/08 0.8

90 01/19/08 9.6

91 01/25/08 0.4

92 01/31/08 0.3

93 02/06/08 1.3

94 02/12/08 3.0

95 02/18/08 5.3

96 02/24/08 2.8

97 03/01/08 3.0


98 03/07/08 3.6

99 03/13/08 2.7

100 03/19/08 7.7

101 03/25/08 13.5

102 03/31/08 5.5

103 04/06/08 9.5

104 04/12/08 19.8

105 04/18/08 15.3

106 04/24/08 18.2

107 04/30/08 19.4

108 05/06/08 18.6

109 05/12/08 21.3

110 05/18/08 16.8

111 05/24/08 18.2

112 05/30/08 6.6

113 06/05/08 32.6

114 06/11/08 20.7

115 06/17/08 22.5

116 06/23/08 28.2

117 06/29/08 25.2

118 07/05/08 22.2

119 07/11/08 19.3

120 07/17/08 22.6

121 07/23/08 8.0

122 08/04/08 12.2

123 08/10/08 17.0

124 08/16/08 14.3

125 08/22/08 18.7

126 08/28/08 10.2

127 09/03/08 13.4

128 09/09/08 20.6

129 09/15/08 13.0

130 09/21/08 11.0

131 09/27/08 11.0

132 10/03/08 12.7

133 10/09/08 6.3

134 10/15/08 5.9

135 10/21/08 4.8

136 10/27/08 31.6

137 11/02/08 9.6

138 11/08/08 9.9

139 11/14/08 9.4

140 11/20/08 15.1

141 11/26/08 4.8

142 12/02/08 2.0

143 12/08/08 5.7

144 12/14/08 5.7

145 12/20/08 2.7

146 12/26/08 1.4

147 01/01/09 2.7

148 01/07/09 1.8


149 01/13/09 3.6

150 01/19/09 10.7

151 01/25/09 2.9

152 01/31/09 5.2

153 02/06/09 6.5

154 02/12/09 1.6

155 02/18/09 5.0

156 02/24/09 7.3

157 03/02/09 17.9

158 03/08/09 9.5

159 03/14/09 9.8

160 03/20/09 17.6

161 03/26/09 17.8

162 04/01/09 12.1

163 04/07/09 10.5

164 04/13/09 6.0

165 04/19/09 5.1

166 04/25/09 6.0

167 05/01/09 11.3

168 05/07/09 12.2

169 05/13/09 12.9

170 05/19/09 9.6

171 05/25/09 11.4

172 05/31/09 7.2

173 06/06/09 12.9

174 06/12/09 7.3

175 06/18/09 8.9

176 06/24/09 15.4

177 06/30/09 10.6


Descriptive Statistics: PM10 Concentrations

Total

Variable Count N N* CumN Percent CumPct Mean SE Mean

PM10 Concentrations 177 177 0 177 100 100 11.625 0.696

Sum of

Variable TrMean StDev Variance CoefVar Sum Squares

PM10 Concentrations 10.937 9.262 85.791 79.67 2057.700 39020.810

Variable Minimum Q1 Median Q3 Maximum Range IQR

PM10 Concentrations 0.300 4.650 9.800 18.050 71.300 71.000 13.400

N for

Variable Mode Mode Skewness Kurtosis MSSD

PM10 Concentrations 2.7 5 1.91 8.77 41.960

604530150

Median

Mean

1312111098

1st Q uartile 4.650

Median 9.800

3rd Q uartile 18.050

Maximum 71.300

10.251 12.999

8.000 11.403

8.388 10.342

A -Squared 3.28

P-V alue < 0.005

Mean 11.625

StDev 9.262

V ariance 85.791

Skewness 1.91285

Kurtosis 8.77247

N 177

Minimum 0.300

A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev

95% Confidence Intervals

Summary for PM10 Concentrations


7550250

99.9

99

95

90

80706050403020

10

5

1

0.1

7550250

PM10 Concentrations


Pe

rce

nt

PM10 Concentration - 71.3 Repl.

Mean 11.63

StDev 9.262

N 177

AD 3.285

PM10 Concentrations

Mean 11.45

StDev 8.378

N 177

AD 2.989


Normal Distribution Probability Plot

1001010.1

99.9

99

95

90

80706050403020

10

5

1

0.1

1001010.1

PM10 Concentrations


Pe

rce

nt


Loc 2.058

Scale 1.040

N 177

AD 3.744

PM10 Concentrations

Loc 2.055

Scale 1.034

N 177

AD 3.870


Lognormal Probability Plot


100101

99.9

99

95

90

80706050403020

10

5

1

0.1

100101

PM10 Concentrations

PM10 Concentrations (µg/m3) - Threshold

Pe

rce

nt


Loc 2.498

Scale 0.6302

Thresh -3.054

N 177

AD 1.471

PM10 Concentrations

Loc 2.564

Scale 0.5807

Thresh -3.778

N 177

AD 1.524


3-Parameter Lognormal Probability Plot

1001010.10.01

99.999

90807060504030

20

10

5

32

1

0.1

1001010.10.01

PM10 Concentrations


Pe

rce

nt


Mean 11.63

N 177

AD 3.280

PM10 Concentrations

Mean 11.45

N 177

AD 3.586


Exponential Probability Plot


1001010.10.01

99.999

90807060504030

20

10

5

32

1

0.1

1001010.10.01

PM10 Concentrations


Pe

rce

nt


Scale 11.39

Thresh 0.2357

N 177

AD 2.727

PM10 Concentrations

Scale 11.21

Thresh 0.2366

N 177

AD 3.014


2-Parameter Exponential Probability Plot

500-50-100

99.999

90807060504030

20

10

5

32

1

0.1

500-50-100

PM10 Concentrations


Pe

rce

nt


Loc 16.90

Scale 15.37

N 177

AD 16.867

PM10 Concentrations

Loc 15.89

Scale 9.721

N 177

AD 6.735


Smallest Extreme Value Probability Plot


1001010.1

99.999

90807060504030

20

10

5

32

1

0.1

1001010.1

PM10 Concentrations


Pe

rce

nt


Shape 1.258

Scale 12.48

N 177

AD 0.796

PM10 Concentrations

Shape 1.311

Scale 12.38

N 177

AD 0.955


Weibull Probability Plot

1001010.10.01

99.999

90807060504030

20

10

5

32

1

0.1

1001010.10.01

PM10 Concentrations


Pe

rce

nt


Shape 1.193

Scale 12.06

Thresh 0.2221

N 177

AD 1.012

PM10 Concentrations

Shape 1.260

Scale 12.08

Thresh 0.1665

N 177

AD 1.066


3-Parameter Weibull Probability Plot


806040200

99.9

99

9897

95

90

80

70605040302010

10.1

806040200

PM10 Concentrations


Pe

rce

nt


Loc 7.649

Scale 6.562

N 177

AD 1.643

PM10 Concentrations

Loc 7.603

Scale 6.446

N 177

AD 1.761


Largest Extreme Value Probability Plot

6040200-20

99.9

99

95

80

50

20

5

1

0.1

6040200-20

PM10 Concentrations


Pe

rce

nt


Loc 10.78

Scale 4.937

N 177

AD 2.809

PM10 Concentrations

Loc 10.76

Scale 4.818

N 177

AD 2.906


Logistic Probability Plot


10001001010.1

99.9

99

95

90

80706050403020

10

5

1

0.1

10001001010.1

PM10 Concentrations


Pe

rce

nt


Loc 2.169

Scale 0.5722

N 177

AD 2.742

PM10 Concentrations

Loc 2.169

Scale 0.5696

N 177

AD 2.832


Loglogistic Probability Plot

100101

99.9

99

95

90

80706050403020

10

5

1

0.1

100101

PM10 Concentrations


Pe

rce

nt


Loc 2.407

Scale 0.4216

Thresh -1.902

N 177

AD 1.824

PM10 Concentrations

Loc 2.436

Scale 0.4060

Thresh -2.184

N 177

AD 1.864


3-Parameter Loglogistic Probability Plot


1001010.1

99.9

99

9590807060504030

20

10

5

32

1

0.1


Pe

rce

nt

Shape 1.409

Scale 8.251

N 177

AD 1.082

Gamma Probability PlotAll PM10 Concentrations

1001010.1

99.9

99

9590807060504030

20

10

5

32

1

0.1


Pe

rce

nt

Shape 1.449

Scale 7.901

N 177

AD 1.224

Gamma Probability PlotPM10 Concentrations with 71.3 Replaced by 40.3


1001010.1

99.9

99

9590807060504030

20

10

5

32

1

0.1


Pe

rce

nt

Shape 1.360

Scale 8.490

Thresh 0.08148

N 177

AD 1.144

3-Parameter Gamma Probability PlotAll PM10 Concentrations

1001010.1

99.9

99

9590807060504030

20

10

5

32

1

0.1


Pe

rce

nt

Shape 1.415

Scale 8.052

Thresh 0.05401

N 177

AD 1.261

3-Parameter Gamma Probability PlotPM10 Concentrations with 71.3 Replaced by 40.3


Probability Density Function

Weibull with shape = 1.258 and scale = 12.48

x f( x )

40.3 0.0017264

Cumulative Distribution Function


x P( X <= x )

40.3 0.987343



x f( x )

71.3 0.0000204



x P( X <= x )

71.3 0.999871



x f( x )

40.3 0.0013918



x P( X <= x )

40.3 0.990895



x f( x )

71.3 0.0000089




x P( X <= x )

71.3 0.999951

APPENDIX D

AVERAGE AND MAXIMUM MINING RATES

Rosemont AERMOD Modeling Protocol D-1

Table D.1 Annual Mining and Haul Truck Process Rates

Year

Mining Process Rates (tons/year) Haul Truck Process Rates (VMT/year)

Ore Waste Total Inside the

Pit Outside the

Pit Total

PP-2 0 1,688,000 1,688,000 1,279 21,489 22,768

PP-1 10,665,000 62,231,000 72,896,000 151,724 1,329,627 1,481,351

1 42,172,000 72,821,000 114,993,000 495,174 1,741,939 2,237,113

2 42,127,000 72,242,000 114,369,000 573,472 1,571,449 2,144,921

3 37,005,000 72,370,000 109,375,000 681,809 1,234,790 1,916,599

4 31,277,000 78,094,000 109,371,000 793,148 1,390,710 2,183,858

5 29,197,000 80,177,000 109,374,000 1,169,202 1,624,041 2,793,243

6 37,134,000 71,241,000 108,375,000 783,422 1,214,853 1,998,276

7 27,376,000 81,998,000 109,374,000 924,290 1,311,106 2,235,396

8 27,376,000 81,996,000 109,372,000 723,846 908,705 1,632,551

9 27,376,000 81,994,000 109,370,000 864,579 1,114,705 1,979,284

10 27,376,000 81,500,000 108,876,000 1,085,986 1,129,025 2,215,011

11 27,376,000 77,000,000 104,376,000 1,180,062 1,267,800 2,447,862

12 27,376,000 68,000,000 95,376,000 1,367,410 1,334,886 2,702,296

13 27,376,000 77,999,000 105,375,000 1,044,548 1,234,969 2,279,518

14 27,376,000 64,999,000 92,375,000 1,149,575 1,104,718 2,254,294

15 27,376,000 51,998,000 79,374,000 1,210,908 984,951 2,195,859

16 27,376,000 40,513,000 67,889,000 1,289,484 841,752 2,131,237

17 27,376,000 4,927,000 32,303,000 643,804 241,285 885,089

18 27,376,000 1,434,000 28,810,000 600,867 171,088 771,955

19 27,376,000 144,000 27,520,000 630,667 164,425 795,092

20 27,376,000 4,368,000 31,744,000 815,388 297,982 1,113,370

21 2,870,000 15,431,000 18,301,000 534,813 201,104 735,917

Rosemont AERMOD Modeling Protocol D-2

Table D.2 Maximum Daily Mining and Haul Truck Process Rates

Year

Mining Process Rates (tons/day) Haul Truck Process Rates (VMT/day)

Average Maximum a Average Maximum

a

Ore Waste Total Ore Waste Total Inside the

Pit Outside the Pit

Total Inside the

Pit Outside the Pit

Total

PP-2 0 4,625 4,625 0 4,625 4,625 14 235 250 14 235 250

PP-1 29,219 170,496 199,715 29,219 170,496 199,715 416 3,643 4,058 416 3,643 4,058

1 115,540 199,510 315,049 115,540 199,510 315,049 1,357 4,772 6,129 1,357 4,772 6,129

2 115,416 197,923 313,340 138,500 237,508 376,008 1,571 4,305 5,876 1,885 5,166 7,052

3 101,384 198,274 299,658 121,660 237,929 359,589 1,868 3,383 5,251 2,242 4,060 6,301

4 85,690 213,956 299,647 102,828 256,747 359,576 2,173 3,810 5,983 2,608 4,572 7,180

5 79,992 219,663 299,655 95,990 263,596 359,586 3,203 4,449 7,653 3,844 5,339 9,183

6 101,737 195,181 296,918 122,084 234,217 356,301 2,146 3,328 5,475 2,576 3,994 6,570

7 75,003 224,652 299,655 90,003 269,582 359,586 2,532 3,592 6,124 3,039 4,310 7,349

8 75,003 224,647 299,649 90,003 269,576 359,579 1,983 2,490 4,473 2,380 2,988 5,367

9 75,003 224,641 299,644 90,003 269,569 359,573 2,369 3,054 5,423 2,842 3,665 6,507

10 75,003 223,288 298,290 90,003 267,945 357,948 2,975 3,093 6,069 3,570 3,712 7,282

11 75,003 210,959 285,962 90,003 253,151 343,154 3,233 3,473 6,706 3,880 4,168 8,048

12 75,003 186,301 261,304 90,003 223,562 313,565 3,746 3,657 7,404 4,496 4,389 8,884

13 75,003 213,696 288,699 90,003 256,435 346,438 2,862 3,383 6,245 3,434 4,060 7,494

14 75,003 178,079 253,082 90,003 213,695 303,699 3,150 3,027 6,176 3,779 3,632 7,411

15 75,003 142,460 217,463 90,003 170,952 260,956 3,318 2,698 6,016 3,981 3,238 7,219

16 75,003 110,995 185,997 90,003 133,193 223,197 3,533 2,306 5,839 4,239 2,767 7,007

17 75,003 13,499 88,501 90,003 16,198 106,202 1,764 661 2,425 2,117 793 2,910

18 75,003 3,929 78,932 90,003 4,715 94,718 1,646 469 2,115 1,975 562 2,538

19 75,003 395 75,397 90,003 473 90,477 1,728 450 2,178 2,073 541 2,614

20 75,003 11,967 86,970 90,003 14,361 104,364 2,234 816 3,050 2,681 980 3,660

21 7,863 42,277 50,140 9,436 50,732 60,168 5,861 2,204 8,065 7,033 2,645 9,678 a Maximum mining process rates are calculated by adding a 20% maximum capacity factor to the average process rates (except for Years PP-2, PP-2, and 1 when maximum

process rates are not expected to exceed average process rates).

APPENDIX E

CORRESPONDENCE WITH ADEQ

Rosemont AERMOD Modeling Protocol E-1


Rural Arizona

Example Background Concentrations

Pollutant Averaging

Time

Ambient Data Background

Value

(g/m3)

Standard

(g/m3)

1999 2000 2001

NO2a annual --- --- --- 4 100

COb

1-hour --- --- --- 582 40,000

8-hour --- --- --- 582 10,000

SO2

3-hour 43 14 15 43 1,300

24-hour 17 7 8 17 365

annual 2 1 3 3 80

a Long-term average value (0.002 ppm) of several monitors located near power plants in rural areas of Arizona

b Typical continental ambient CO background value (0.5 ppm) used in most regional models

c Max. values over 3-year period from Page monitoring station (Coconino County)

APPENDIX F

IN-STACK NO2/NOx RATIO JUSTIFICATION

APPENDIX G

CHANGES IN EMISSIONS

FROM PRIOR SUBMITTALS

Rosemont AERMOD Modeling Protocol G-1

Table G.1 Comparison of Non-Fugitive Emissions from the RCP - Proposed Action

Emission Category Non-Fugitive Emissions (tpy)

PM/TSP PM10 PM2.5 CO NOX SO2 VOC H2SO4 CO2 CH4 N2O HAPs

Year 1

Prior to Mitigation and Refinement a 72.44 66.81 53.68 9.00 16.76 0.06 1.51 0.02 6,040.20 0.25 0.05 0.05

After Mitigation and Refinement b 78.46 39.03 10.23 9.00 16.76 0.06 1.51 0.02 6,040.20 0.25 0.05 0.05

Change in Emissions 6.02 -27.78 -43.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Year 5

Prior to Mitigation and Refinement 72.44 66.81 53.68 9.00 16.76 0.06 1.51 0.02 6,040.20 0.25 0.05 0.05

After Mitigation and Refinement 78.46 39.03 10.23 9.00 16.76 0.06 1.51 0.02 6,040.20 0.25 0.05 0.05


Year 10




Year 15




Year 20




a Emission totals are from "Rosemont Copper Company, Application for a Class II Permit and Emission Inventory Information, Rosemont Copper Project, Southeastern Arizona"

submitted to ADEQ on November 15, 2011. b Emission totals are from "Rosemont Copper Company, Amendment to: Application for a Class II Permit and Emission Inventory Information, Rosemont Copper Project,

Southeastern Arizona" submitted to ADEQ on March 19, 2012.


Table G.2 Comparison of Fugitive Emissions from the RCP - Proposed Action

Emission Category Fugitive Emissions (tpy)


Year 1

Prior to Mitigation and Refinement a 2,851.09 785.29 87.92 635.83 161.33 18.98 3.77 0.00 5,375.60 0.22 0.04 3.32

After Mitigation and Refinement b 2,791.22 765.20 88.32 635.83 161.33 18.98 3.77 0.00 5,375.60 0.22 0.04 3.32

Change in Emissions -59.87 -20.10 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Year 5

Prior to Mitigation and Refinement 3,297.90 894.91 98.84 606.22 153.82 18.10 3.77 0.00 5,125.23 0.21 0.04 3.32

After Mitigation and Refinement 3,238.04 874.81 99.24 606.22 153.82 18.10 3.77 0.00 5,125.23 0.21 0.04 3.32


Year 10




Year 15




Year 20



Change in Emissions -59.84 -20.09 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 a Emission totals are from "Rosemont Copper Company, Application for a Class II Permit and Emission Inventory Information, Rosemont Copper Project, Southeastern Arizona"



Table G.3 Comparison of Tailpipe Emissions from the RCP - Proposed Action


Emission Category Tailpipe Emissions (tpy)


Year 1

Prior to Mitigation and Refinement a 33.85 33.85 33.85 831.98 1,086.65 1.54 78.70 -- 163,786.04 -- -- --

After Mitigation and Refinement b 29.40 29.40 29.40 831.98 1,016.69 1.54 72.84 -- 163,786.04 -- -- --

Change in Emissions -4.45 -4.45 -4.45 0.00 -69.96 0.00 -5.86 -- 0.00 -- -- --

Year 5

Prior to Mitigation and Refinement 33.84 33.84 33.84 829.16 1,086.32 1.54 78.55 -- 163,247.91 -- -- --

After Mitigation and Refinement 29.39 29.39 29.39 829.16 1,016.36 1.54 72.69 -- 163,247.91 -- -- --


Year 10




Year 15




Year 20


After Mitigation and Refinement 29.14 29.14 29.14 795.06 999.01 1.54 70.85 -- 156,725.16 -- -- --


a Emission totals are from "Rosemont Copper Company, Application for a Class II Permit and Emission Inventory Information, Rosemont Copper Project, Southeastern Arizona"



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